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Matt Spencer
2026-07-11 20:16:13 +01:00
parent b66deea825
commit b506c1d133
64 changed files with 4 additions and 12665 deletions
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point_lio_ros2/.gitignore
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/.idea/
/cmake-build-debug/
*.pcd
/Log/pos_log.csv
point_lio_ros2/.gitmodules
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[submodule "ikd-Tree"]
path = ikd-Tree
url = https://github.com/hku-mars/ikd-Tree.git
branch = fast_lio
[submodule "include/IKFoM"]
path = include/IKFoM
url = https://github.com/hku-mars/IKFoM.git
branch = toolkit
point_lio_ros2/CMakeLists.txt
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cmake_minimum_required(VERSION 3.5)
project(point_lio)
# Use C++14
if(NOT CMAKE_CXX_STANDARD)
set(CMAKE_CXX_STANDARD 14)
endif()
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_CXX_EXTENSIONS OFF)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fexceptions" )
# Compiler settings for performance and threading
add_definitions(-DROOT_DIR=\"${CMAKE_CURRENT_SOURCE_DIR}/\")
add_compile_options(-O3 -pthread -fexceptions)
# Processor count and definitions
message(STATUS "Current CPU architecture: ${CMAKE_SYSTEM_PROCESSOR}")
if(CMAKE_SYSTEM_PROCESSOR MATCHES "(x86)|(X86)|(amd64)|(AMD64)" )
include(ProcessorCount)
ProcessorCount(N)
message(STATUS "Processor number: ${N}")
if(N GREATER 5)
add_definitions(-DMP_EN)
add_definitions(-DMP_PROC_NUM=4)
message(STATUS "core for MP: 3")
elseif(N GREATER 3)
math(EXPR PROC_NUM "${N} - 2")
add_definitions(-DMP_EN)
add_definitions(-DMP_PROC_NUM="${PROC_NUM}")
message(STATUS "core for MP: ${PROC_NUM}")
else()
add_definitions(-DMP_PROC_NUM=1)
endif()
else()
add_definitions(-DMP_PROC_NUM=1)
endif()
# Find packages
find_package(ament_cmake REQUIRED)
find_package(rclcpp REQUIRED)
find_package(rclpy REQUIRED)
find_package(geometry_msgs REQUIRED)
find_package(nav_msgs REQUIRED)
find_package(sensor_msgs REQUIRED)
find_package(pcl_ros REQUIRED)
find_package(pcl_conversions REQUIRED)
find_package(tf2_ros REQUIRED)
find_package(visualization_msgs REQUIRED)
# find_package(livox_ros_driver2 REQUIRED)
find_package(Eigen3 REQUIRED)
# OpenMP
find_package(OpenMP QUIET)
if(OPENMP_FOUND)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
endif()
find_package(PythonLibs REQUIRED) # Consider using Pybind11 for ROS2
find_path(MATPLOTLIB_CPP_INCLUDE_DIRS "matplotlibcpp.h")
include_directories(
include
${EIGEN3_INCLUDE_DIR}
${PYTHON_INCLUDE_DIRS}
)
# Declare a ROS2 executable
add_executable(pointlio_mapping src/laserMapping.cpp include/ikd-Tree/ikd_Tree.cpp src/parameters.cpp src/preprocess.cpp src/Estimator.cpp)
ament_target_dependencies(pointlio_mapping
rclcpp
rclpy
geometry_msgs
nav_msgs
sensor_msgs
pcl_ros
pcl_conversions
tf2_ros
visualization_msgs
# livox_ros_driver2
)
target_link_libraries(pointlio_mapping ${PYTHON_LIBRARIES})
target_include_directories(pointlio_mapping PRIVATE ${PYTHON_INCLUDE_DIRS})
# Install the executable
install(TARGETS
pointlio_mapping
DESTINATION lib/${PROJECT_NAME}
)
install(
DIRECTORY config launch rviz_cfg
DESTINATION share/${PROJECT_NAME}
)
# Export dependencies
ament_export_dependencies(rclcpp
rclpy
geometry_msgs
nav_msgs
sensor_msgs
pcl_ros
pcl_conversions
tf2_ros
visualization_msgs
# livox_ros_driver2
Eigen3
)
ament_package()
point_lio_ros2/LICENSE
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GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
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Also add information on how to contact you by electronic and paper mail.
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Yoyodyne, Inc., hereby disclaims all copyright interest in the program
`Gnomovision' (which makes passes at compilers) written by James Hacker.
<signature of Ty Coon>, 1 April 1989
Ty Coon, President of Vice
This General Public License does not permit incorporating your program into
proprietary programs. If your program is a subroutine library, you may
consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License.
point_lio_ros2/Log/guide.md
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Here saved the debug records which can be drew by the ../log/plot.py. The record function can be found frm the MACRO: DEBUG_FILE_DIR(name) in common_lib.h.
point_lio_ros2/Log/imu.txt
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point_lio_ros2/Log/imu_pbp.txt
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point_lio_ros2/Log/mat_out.txt
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point_lio_ros2/Log/plot.py
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# import matplotlib
# matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
a_out=np.loadtxt('mat_out.txt')
#######for normal#######
fig, axs = plt.subplots(3,2)
lab_out = ['', 'out-x', 'out-y', 'out-z']
plot_ind = range(7,10)
time=a_out[:,0]
axs[0,0].set_title('Attitude')
axs[1,0].set_title('Translation')
axs[2,0].set_title('Velocity')
axs[0,1].set_title('bg')
axs[1,1].set_title('ba')
axs[2,1].set_title('Gravity')
for i in range(1,4):
for j in range(6):
axs[j%3, j//3].plot(time, a_out[:,i+j*3],'.-', label=lab_out[i])
for j in range(6):
axs[j%3, j//3].grid()
axs[j%3, j//3].legend()
plt.grid()
#######for normal#######
#### Draw IMU data
#fig, axs = plt.subplots(2)
#imu=np.loadtxt('imu_pbp.txt')
#time=imu[:,0]
#axs[0].set_title('Gyroscope')
#axs[1].set_title('Accelerameter')
#lab_1 = ['gyr-x', 'gyr-y', 'gyr-z']
#lab_2 = ['acc-x', 'acc-y', 'acc-z']
#for i in range(3):
#if i==1:
# axs[0].plot(time, imu[:,i+1],'.-', label=lab_1[i])
# axs[1].plot(time, imu[:,i+4],'.-', label=lab_2[i])
#for i in range(2):
#axs[i].set_xlim(386,389)
# axs[i].grid()
# axs[i].legend()
#plt.grid()
plt.show()
point_lio_ros2/Log/plot_imu.py
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# import matplotlib
# matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
#### Draw IMU data
fig, axs = plt.subplots(2)
imu=np.loadtxt('imu_pbp.txt')
time=imu[:,0]
axs[0].set_title('Gyroscope')
axs[1].set_title('Accelerameter')
lab_1 = ['gyr-x', 'gyr-y', 'gyr-z']
lab_2 = ['acc-x', 'acc-y', 'acc-z']
for i in range(3):
#if i==1:
axs[0].plot(time, imu[:,i+1],'.-', label=lab_1[i])
axs[1].plot(time, imu[:,i+4],'.-', label=lab_2[i])
for i in range(2):
#axs[i].set_xlim(386,389)
axs[i].grid()
axs[i].legend()
plt.grid()
#fig, axs = plt.subplots(5)
#axs[0].set_title('miss')
#axs[1].set_title('miss')
#axs[2].set_title('miss')
#axs[3].set_title('miss')
#axs[4].set_title('miss')
#len_time1 = np.arange(0,1977)
#len_time2 = np.arange(1977, 3954)
#len_time3 = np.arange(3954,5931)
#len_time4 = np.arange(5931,7908)
#len_time5 = np.arange(7908,9885)
#if i==1:
#axs[0].plot(len_time1, time[0:1977],'.-', label='check')
#axs[1].plot(len_time2, time[1977:3954],'.-', label='check')
#axs[2].plot(len_time3, time[3954:5931],'.-', label='check')
#axs[3].plot(len_time4, time[5931:7908],'.-', label='check')
#axs[4].plot(len_time5, time[7908:9885],'.-', label='check')
#axs[i].set_xlim(386,389)
#axs[0].grid()
#axs[0].legend()
#axs[1].grid()
#axs[1].legend()
#axs[2].grid()
#axs[2].legend()
#axs[3].grid()
#axs[3].legend()
#axs[4].grid()
#axs[4].legend()
#plt.grid()
#fig, axs = plt.subplots(5)
#axs[0].set_title('miss')
#axs[1].set_title('miss')
#axs[2].set_title('miss')
#axs[3].set_title('miss')
#axs[4].set_title('miss')
#len_time1 = np.arange(9885,9885+1977)
#len_time2 = np.arange(9885+1977,9885+3954)
#len_time3 = np.arange(9885+3954,9885+5931)
#len_time4 = np.arange(9885+5931,9885+7908)
#len_time5 = np.arange(9885+7908,9885+9885)
#if i==1:
#axs[0].plot(len_time1, time[9885+0:9885+1977],'.-', label='check')
#axs[1].plot(len_time2, time[9885+1977:9885+3954],'.-', label='check')
#axs[2].plot(len_time3, time[9885+3954:9885+5931],'.-', label='check')
#axs[3].plot(len_time4, time[9885+5931:9885+7908],'.-', label='check')
#axs[4].plot(len_time5, time[9885+7908:9885+9885],'.-', label='check')
#axs[i].set_xlim(386,389)
#axs[0].grid()
#axs[0].legend()
#axs[1].grid()
#axs[1].legend()
#axs[2].grid()
#axs[2].legend()
#axs[3].grid()
#axs[3].legend()
#axs[4].grid()
#axs[4].legend()
#plt.grid()
# #### Draw time calculation
# plt.figure(3)
# fig = plt.figure()
# font1 = {'family' : 'Times New Roman',
# 'weight' : 'normal',
# 'size' : 12,
# }
# c="red"
# a_out1=np.loadtxt('Log/mat_out_time_indoor1.txt')
# a_out2=np.loadtxt('Log/mat_out_time_indoor2.txt')
# a_out3=np.loadtxt('Log/mat_out_time_outdoor.txt')
# # n = a_out[:,1].size
# # time_mean = a_out[:,1].mean()
# # time_se = a_out[:,1].std() / np.sqrt(n)
# # time_err = a_out[:,1] - time_mean
# # feat_mean = a_out[:,2].mean()
# # feat_err = a_out[:,2] - feat_mean
# # feat_se = a_out[:,2].std() / np.sqrt(n)
# ax1 = fig.add_subplot(111)
# ax1.set_ylabel('Effective Feature Numbers',font1)
# ax1.boxplot(a_out1[:,2], showfliers=False, positions=[0.9])
# ax1.boxplot(a_out2[:,2], showfliers=False, positions=[1.9])
# ax1.boxplot(a_out3[:,2], showfliers=False, positions=[2.9])
# ax1.set_ylim([0, 3000])
# ax2 = ax1.twinx()
# ax2.spines['right'].set_color('red')
# ax2.set_ylabel('Compute Time (ms)',font1)
# ax2.yaxis.label.set_color('red')
# ax2.tick_params(axis='y', colors='red')
# ax2.boxplot(a_out1[:,1]*1000, showfliers=False, positions=[1.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.boxplot(a_out2[:,1]*1000, showfliers=False, positions=[2.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.boxplot(a_out3[:,1]*1000, showfliers=False, positions=[3.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.set_xlim([0.5, 3.5])
# ax2.set_ylim([0, 100])
# plt.xticks([1,2,3], ('Outdoor Scene', 'Indoor Scene 1', 'Indoor Scene 2'))
# # # print(time_se)
# # # print(a_out3[:,2])
# plt.grid()
# plt.savefig("time.pdf", dpi=1200)
plt.show()
point_lio_ros2/Log/plot_out.py
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# import matplotlib
# matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
# a_pre=np.loadtxt('mat_pre.txt')
a_out=np.loadtxt('mat_out.txt')
if((a_out.shape[1] != 19) & (a_out.shape[1] != 20)):
######for ikfom
fig, axs = plt.subplots(4,2)
#lab_pre = ['', 'pre-x', 'pre-y', 'pre-z']
lab_out = ['', 'out-x', 'out-y', 'out-z']
plot_ind = range(7,10)
time=a_out[:,0]
axs[0,0].set_title('Attitude')
axs[1,0].set_title('Translation')
axs[2,0].set_title('Velocity')
axs[3,0].set_title('Angular velocity')
axs[0,1].set_title('Acceleration')
axs[1,1].set_title('Gravity')
axs[2,1].set_title('bg')
axs[3,1].set_title('ba')
for i in range(1,4):
for j in range(8):
#axs[j%4, j//4].plot(time, a_pre[:,i+j*3],'.-', label=lab_pre[i])
axs[j%4, j//4].plot(time, a_out[:,i+j*3],'.-', label=lab_out[i])
for j in range(8):
# axs[j].set_xlim(386,389)
axs[j%4, j//4].grid()
axs[j%4, j//4].legend()
plt.grid()
######for ikfom#######
else:
#######for normal#######
fig, axs = plt.subplots(3,2)
lab_pre = ['', 'pre-x', 'pre-y', 'pre-z']
lab_out = ['', 'out-x', 'out-y', 'out-z']
plot_ind = range(7,10)
time=a_out[:,0]
time1 = a_pre[:,0]
axs[0,0].set_title('Attitude')
axs[1,0].set_title('Translation')
axs[2,0].set_title('Velocity')
axs[0,1].set_title('bg')
axs[1,1].set_title('ba')
axs[2,1].set_title('Gravity')
for i in range(1,4):
for j in range(6):
axs[j%3, j/3].plot(time1, a_pre[:,i+j*3],'.-', label=lab_pre[i])
axs[j%3, j/3].plot(time, a_out[:,i+j*3],'.-', label=lab_out[i])
for j in range(6):
# axs[j].set_xlim(386,389)
axs[j%3, j//3].grid()
axs[j%3, j//3].legend()
plt.grid()
#######for normal#######
#### Draw IMU data
fig, axs = plt.subplots(2)
imu=np.loadtxt('imu_pbp.txt')
time=imu[:,0]
axs[0].set_title('Gyroscope')
axs[1].set_title('Accelerameter')
lab_1 = ['gyr-x', 'gyr-y', 'gyr-z']
lab_2 = ['acc-x', 'acc-y', 'acc-z']
for i in range(3):
#if i==1:
axs[0].plot(time, imu[:,i+1],'.-', label=lab_1[i])
axs[1].plot(time, imu[:,i+4],'.-', label=lab_2[i])
for i in range(2):
#axs[i].set_xlim(386,389)
axs[i].grid()
axs[i].legend()
plt.grid()
#fig, axs = plt.subplots(5)
#axs[0].set_title('miss')
#axs[1].set_title('miss')
#axs[2].set_title('miss')
#axs[3].set_title('miss')
#axs[4].set_title('miss')
#len_time1 = np.arange(0,1977)
#len_time2 = np.arange(1977, 3954)
#len_time3 = np.arange(3954,5931)
#len_time4 = np.arange(5931,7908)
#len_time5 = np.arange(7908,9885)
#if i==1:
#axs[0].plot(len_time1, time[0:1977],'.-', label='check')
#axs[1].plot(len_time2, time[1977:3954],'.-', label='check')
#axs[2].plot(len_time3, time[3954:5931],'.-', label='check')
#axs[3].plot(len_time4, time[5931:7908],'.-', label='check')
#axs[4].plot(len_time5, time[7908:9885],'.-', label='check')
#axs[i].set_xlim(386,389)
#axs[0].grid()
#axs[0].legend()
#axs[1].grid()
#axs[1].legend()
#axs[2].grid()
#axs[2].legend()
#axs[3].grid()
#axs[3].legend()
#axs[4].grid()
#axs[4].legend()
#plt.grid()
#fig, axs = plt.subplots(5)
#axs[0].set_title('miss')
#axs[1].set_title('miss')
#axs[2].set_title('miss')
#axs[3].set_title('miss')
#axs[4].set_title('miss')
#len_time1 = np.arange(9885,9885+1977)
#len_time2 = np.arange(9885+1977,9885+3954)
#len_time3 = np.arange(9885+3954,9885+5931)
#len_time4 = np.arange(9885+5931,9885+7908)
#len_time5 = np.arange(9885+7908,9885+9885)
#if i==1:
#axs[0].plot(len_time1, time[9885+0:9885+1977],'.-', label='check')
#axs[1].plot(len_time2, time[9885+1977:9885+3954],'.-', label='check')
#axs[2].plot(len_time3, time[9885+3954:9885+5931],'.-', label='check')
#axs[3].plot(len_time4, time[9885+5931:9885+7908],'.-', label='check')
#axs[4].plot(len_time5, time[9885+7908:9885+9885],'.-', label='check')
#axs[i].set_xlim(386,389)
#axs[0].grid()
#axs[0].legend()
#axs[1].grid()
#axs[1].legend()
#axs[2].grid()
#axs[2].legend()
#axs[3].grid()
#axs[3].legend()
#axs[4].grid()
#axs[4].legend()
#plt.grid()
# #### Draw time calculation
# plt.figure(3)
# fig = plt.figure()
# font1 = {'family' : 'Times New Roman',
# 'weight' : 'normal',
# 'size' : 12,
# }
# c="red"
# a_out1=np.loadtxt('Log/mat_out_time_indoor1.txt')
# a_out2=np.loadtxt('Log/mat_out_time_indoor2.txt')
# a_out3=np.loadtxt('Log/mat_out_time_outdoor.txt')
# # n = a_out[:,1].size
# # time_mean = a_out[:,1].mean()
# # time_se = a_out[:,1].std() / np.sqrt(n)
# # time_err = a_out[:,1] - time_mean
# # feat_mean = a_out[:,2].mean()
# # feat_err = a_out[:,2] - feat_mean
# # feat_se = a_out[:,2].std() / np.sqrt(n)
# ax1 = fig.add_subplot(111)
# ax1.set_ylabel('Effective Feature Numbers',font1)
# ax1.boxplot(a_out1[:,2], showfliers=False, positions=[0.9])
# ax1.boxplot(a_out2[:,2], showfliers=False, positions=[1.9])
# ax1.boxplot(a_out3[:,2], showfliers=False, positions=[2.9])
# ax1.set_ylim([0, 3000])
# ax2 = ax1.twinx()
# ax2.spines['right'].set_color('red')
# ax2.set_ylabel('Compute Time (ms)',font1)
# ax2.yaxis.label.set_color('red')
# ax2.tick_params(axis='y', colors='red')
# ax2.boxplot(a_out1[:,1]*1000, showfliers=False, positions=[1.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.boxplot(a_out2[:,1]*1000, showfliers=False, positions=[2.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.boxplot(a_out3[:,1]*1000, showfliers=False, positions=[3.1],boxprops=dict(color=c),capprops=dict(color=c),whiskerprops=dict(color=c))
# ax2.set_xlim([0.5, 3.5])
# ax2.set_ylim([0, 100])
# plt.xticks([1,2,3], ('Outdoor Scene', 'Indoor Scene 1', 'Indoor Scene 2'))
# # # print(time_se)
# # # print(a_out3[:,2])
# plt.grid()
# plt.savefig("time.pdf", dpi=1200)
plt.show()
point_lio_ros2/Log/pos_log.txt
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point_lio_ros2/README.md
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# Point-LIO-ROS2 (with Unitree Unilidar L1/L2 support)
## Point-LIO: Robust High-Bandwidth Lidar-Inertial Odometry
*(Pay attention to modifying the parameters for IMU in .yaml file, according to the IMU you use.)*
ROS2 port of [Point-LIO](https://github.com/hku-mars/Point-LIO) with support for the Unitree Unilidar L1/L2 as implemented on the ROS1 [point_lio_unilidar](https://github.com/unitreerobotics/point_lio_unilidar) package.
## 1. Introduction
`Point-LIO` is a robust and high-bandwidth lidar inertial odometry (LIO) with the capability to provide accurate, high-frequency odometry and reliable mapping under severe vibrations and aggressive motions. If you need further information about the `Point-LIO` algorithm, you can refer to their official website and paper:
- <https://github.com/hku-mars/Point-LIO>
- [Point‐LIO: Robust High‐Bandwidth Light Detection and Ranging Inertial Odometry](https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202200459)
<div align="center">
<div align="center">
<img src="https://github.com/hku-mars/Point-LIO/raw/master/image/toc4.png" width = 75% >
</div>
<font color=#a0a0a0 size=2>The framework and key points of the Point-LIO.</font>
</div>
<br>
The codes of this repo are contributed by:
[Dongjiao He (贺东娇)](https://github.com/Joanna-HE) and [Wei Xu (徐威)](https://github.com/XW-HKU) as well as [Daniel Florea](https://github.com/dfloreaa) for the ROS2 port and Unitree LiDAR support.
**Important notes:**
A. Please make sure the IMU and LiDAR are **Synchronized**, that's important.
B. Please obtain the saturation values of your used IMU (i.e., accelerator and gyroscope), and the units of the accelerator of your used IMU, then modify the .yaml file according to those settings, including values of 'satu_acc', 'satu_gyro', 'acc_norm'. That's improtant.
C. The warning message "Failed to find match for field 'time'." means the timestamps of each LiDAR points are missed in the rosbag file. That is important because Point-LIO processes at the sampling time of each LiDAR point.
D. We recommend to set the **extrinsic_est_en** to false if the extrinsic is given. As for the extrinsic initiallization, please refer to our recent work: [**Robust and Online LiDAR-inertial Initialization**](https://github.com/hku-mars/LiDAR_IMU_Init).
E. If a high odometry output frequency without downsample is required, set `publish_odometry_without_downsample` as true. Then the warning message of tf `"TF_REPEATED_DATA"` will pop up in the terminal window, because the time interval between two publish odometery is too small. The following command could be used to suppress this warning to a smaller frequency:
in your `catkin_ws/src`,
```bash
git clone --branch throttle-tf-repeated-data-error git@github.com:BadgerTechnologies/geometry2.git
```
Then rebuild, source `setup.bash`, run and then it should be reduced down to once every 10 seconds. If 10 seconds is still too much log output then change the ros::Duration(10.0) to 10000 seconds or whatever you like.
F. If you want to use Point-LIO without imu, set the `"imu_en"` as false, and provide a predefined value of gavity in `"gravity_init"` as true as possible in the yaml file, and keep the `"use_imu_as_input"` as 0.
## **2. Videos**
An set of accompaning videos are available on **YouTube**.
### 2.1 Point-LIO original demo
Original video demostration, straight from the original repo
<div align="center">
<a href="https://youtu.be/oS83xUs42Uw" target="_blank"><img src="https://github.com/hku-mars/Point-LIO/raw/master/image/final.png" width=60% /></a>
</div>
### 2.2 L1 LiDAR
Official demo by Unitree using their [`point_lio_unilidar`](https://github.com/unitreerobotics/point_lio_unilidar) implementation for the L1 LiDAR:
<div align="center">
<a href="https://oss-global-cdn.unitree.com/static/c0bd0ac7d1e147e7a7eaf909f1fc214f.mp4" target="_blank"><img src="https://github.com/unitreerobotics/point_lio_unilidar/raw/main/doc/video.png" width=60% /></a>
</div>
### 2.3 L2 LiDAR
Official demo by Unitree using their [`point_lio_unilidar`](https://github.com/unitreerobotics/point_lio_unilidar) implementation for the L2 LiDAR:
<div align="center">
<a href="https://youtu.be/juAfGrg2xBg?si=IVTWM9shEmHsKKJ_" target="_blank"><img src="https://github.com/unitreerobotics/point_lio_unilidar/raw/main/doc/l2-demo-video-bilibili.png" width=60% /></a>
</div>
## **3. Prerequisites**
### **3.1 Ubuntu and [ROS](https://www.ros.org/)**
We tested our code on Ubuntu 22.04 with Humble. Other versions may have problems of environments to support the Point-LIO, try to avoid using Point-LIO in those systems.
Additional ROS package is required:
- For ROS2 Humble:
```bash
sudo apt-get install ros-humble-pcl-ros
sudo apt-get install ros-humble-pcl-conversions
sudo apt-get install ros-humble-visualization-msgs
```
### **3.2 Eigen**
Following the official [Eigen installation](eigen.tuxfamily.org/index.php?title=Main_Page), or directly install Eigen by:
```bash
sudo apt-get install libeigen3-dev
```
### **3.3 `livox_ros_driver2`**
Follow [livox_ros_driver2 Installation](https://github.com/Livox-SDK/livox_ros_driver2).
*Remarks:*
- Since the Point-LIO supports Livox serials LiDAR, so the **livox_ros_driver2** must be installed and **sourced** before run any Point-LIO launch file.
- How to source? The easiest way is add the line ``` source $Licox_ros_driver_dir$/install/setup.bash ``` to the end of file ``` ~/.bashrc ```, where ``` $Licox_ros_driver_dir$ ``` is the directory of the livox ros driver workspace (should be the ``` ws_livox ``` directory if you completely followed the livox official document).
### 3.4 `unilidar_sdk`
For using lidar `L1`, you should download and build [unilidar_sdk](https://github.com/unitreerobotics/unilidar_sdk) follwing these steps:
```bash
git clone https://github.com/unitreerobotics/unilidar_sdk.git
cd unilidar_sdk/unitree_lidar_ros2
colcon build
```
### 3.5 `unilidar_sdk2`
For using lidar `L2`, you should download and build [unilidar_sdk2](https://github.com/unitreerobotics/unilidar_sdk2) follwing these steps:
```bash
git clone https://github.com/unitreerobotics/unilidar_sdk2.git
cd unilidar_sdk/unitree_lidar_ros2
colcon build
```
## 4. Build
Clone the repository and colcon build:
```bash
mkdir -p catkin_point_lio_unilidar/src
cd catkin_point_lio_unilidar/src
git clone https://github.com/dfloreaa/point_lio_ros2.git
cd ..
colcon build --symlink-install
source install/setup.bash
```
- Remember to source the livox_ros_driver before build (follow 3.3 `livox_ros_driver2`)
- If you want to use a custom build of PCL, add the following line to ~/.bashrc
```export PCL_ROOT={CUSTOM_PCL_PATH}```
## 5. Directly run
### 5.1 For Avia
Connect to your PC to Livox Avia LiDAR by following [Livox-ros-driver installation](https://github.com/Livox-SDK/livox_ros_driver), then
```
cd ~/$Point_LIO_ROS_DIR$
source install/setup.bash
ros2 launch point_lio mapping_avia.launch.py
ros2 launch livox_ros_driver msg_HAP_launch.py
```
- For livox serials, Point-LIO only support the data collected by the ``` msg_HAP_launch.py ``` since only its ``` livox_ros_driver/CustomMsg ``` data structure produces the timestamp of each LiDAR point which is very important for Point-LIO. ``` livox_lidar.launch.py ``` can not produce it right now.
- If you want to change the frame rate, please modify the **publish_freq** parameter in the [msg_HAP_launch.py](https://github.com/Livox-SDK/livox_ros_driver2/blob/master/launch_ROS2/msg_HAP_launch.py) of [Livox-ros-driver](https://github.com/Livox-SDK/livox_ros_driver) before make the livox_ros_driver pakage.
### 5.2 For Livox serials with external IMU
mapping_avia.launch theratically supports mid-70, mid-40 or other livox serial LiDAR, but need to setup some parameters befor run:
Edit ``` config/avia.yaml ``` to set the below parameters:
1. LiDAR point cloud topic name: ``` lid_topic ```
2. IMU topic name: ``` imu_topic ```
3. Translational extrinsic: ``` extrinsic_T ```
4. Rotational extrinsic: ``` extrinsic_R ``` (only support rotation matrix)
- The extrinsic parameters in Point-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. the IMU is the base frame). They can be found in the official manual.
5. Saturation value of IMU's accelerator and gyroscope: ```satu_acc```, ```satu_gyro```
6. The norm of IMU's acceleration according to unit of acceleration messages: ``` acc_norm ```
### 5.3 For Velodyne or Ouster (Velodyne as an example)
Step A: Setup before run
Edit ``` config/velodyne.yaml ``` to set the below parameters:
1. LiDAR point cloud topic name: ``` lid_topic ```
2. IMU topic name: ``` imu_topic ``` (both internal and external, 6-aixes or 9-axies are fine)
3. Set the parameter ```timestamp_unit``` based on the unit of **time** (Velodyne) or **t** (Ouster) field in PoindCloud2 rostopic
4. Line number (we tested 16, 32 and 64 line, but not tested 128 or above): ``` scan_line ```
5. Translational extrinsic: ``` extrinsic_T ```
6. Rotational extrinsic: ``` extrinsic_R ``` (only support rotation matrix)
- The extrinsic parameters in Point-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. the IMU is the base frame).
7. Saturation value of IMU's accelerator and gyroscope: ```satu_acc```, ```satu_gyro```
8. The norm of IMU's acceleration according to unit of acceleration messages: ``` acc_norm ```
Step B: Run below
```
cd ~/$Point_LIO_ROS_DIR$
source install/setup.bash
ros2 launch point_lio mapping_velody16.launch.py
```
Step C: Run LiDAR's ros driver or play rosbag.
### 5.4 For Unitree LiDAR (L1 as an example)
Step A: Run below
```
cd ~/catkin_point_lio_unilidar
source install/setup.bash
ros2 launch point_lio mapping_unilidar_l1.py
```
Step B: Run LiDAR's ros driver or play rosbag.
### 5.5 PCD file save
Set ``` pcd_save_enable ``` in launchfile to ``` 1 ```. All the scans (in global frame) will be accumulated and saved to the file ``` Point-LIO/PCD/scans.pcd ``` after the Point-LIO is terminated. ```pcl_viewer scans.pcd``` can visualize the point clouds.
*Tips for pcl_viewer:*
- change what to visualize/color by pressing keyboard 1,2,3,4,5 when pcl_viewer is running.
```
1 is all random
2 is X values
3 is Y values
4 is Z values
5 is intensity
```
# **6. Examples**
The example datasets could be downloaded through [onedrive](https://connecthkuhk-my.sharepoint.com/:f:/g/personal/hdj65822_connect_hku_hk/EmRJYy4ZfAlMiIJ786ogCPoBcGQ2BAchuXjE5oJQjrQu0Q?e=igu44W). Pay attention that if you want to test on racing_drone.bag, [0.0, 9.810, 0.0] should be input in 'mapping/gravity_init' in avia.yaml, and set the 'start_in_aggressive_motion' as true in the yaml. Because this bag start from a high speed motion. And for PULSAR.bag, we change the measuring range of the gyroscope of the built-in IMU to 17.5 rad/s. Therefore, when you test on this bag, please change 'satu_gyro' to 17.5 in avia.yaml.
## **6.1. Example-1: SLAM on datasets with aggressive motions where IMU is saturated**
<div align="center">
<img src="https://github.com/hku-mars/Point-LIO/raw/master/image/example1.gif" width="40%" />
<img src="https://github.com/hku-mars/Point-LIO/raw/master/image/example2.gif" width="54%" />
</div>
## **6.2. Example-2: Application on FPV and PULSAR**
<div align="center">
<img src="https://github.com/hku-mars/Point-LIO/raw/master/image/example3.gif" width="58%" />
<img src="https://github.com/hku-mars/Point-LIO/raw/master/image/example4.gif" width="35%" />
</div>
PULSAR is a self-rotating UAV actuated by only one motor, [PULSAR](https://github.com/hku-mars/PULSAR)
## 7. Contact us
If you have any questions about this work, please feel free to contact me <dflorea@uc.cl> via email.
point_lio_ros2/config/avia.yaml
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/**:
ros__parameters:
common:
lid_topic: "/livox/lidar"
imu_topic: "/livox/imu"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme)
# the timestamp of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value
preprocess:
lidar_type: 1
scan_line: 6
timestamp_unit: 1 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 1.0
mapping:
imu_en: true
start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.005 # = 1 / frequency of IMU
satu_acc: 3.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 17.5 # the saturation value of IMU's angular velocity. not related to the units (default = 35 rad/s)
acc_norm: 1.0 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.001 # 0.001; 0.01
acc_cov_output: 500.0
gyr_cov_output: 1000.0
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.1 #0.1 # 0.1
imu_meas_omg_cov: 0.1 #0.01 # 0.1
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81.0
fov_degree: 90.0
det_range: 450.0
gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
gravity: [ 0.0, 0.0, -9.810 ] # [0.0, 9.810, 0.0] # gravity to be aligned
gravity_init: [ 0.0, 0.0, -9.810 ] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
extrinsic_T: [ 0.04165, 0.02326, -0.0284 ]
extrinsic_R: [ 1.0, 0.0, 0.0,
0.0, 1.0, 0.0,
0.0, 0.0, 1.0 ]
odometry:
publish_odometry_without_downsample: false
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: false
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.
point_lio_ros2/config/horizon.yaml
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/**:
ros__parameters:
common:
lid_topic: "/livox/lidar"
imu_topic: "/livox/imu"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme),
# the timestamp of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value
preprocess:
lidar_type: 1
scan_line: 6
timestamp_unit: 1 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 4.0
mapping:
imu_en: true
start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.005 # = 1 / frequency of IMU
satu_acc: 3.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35.0 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 1.0 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.01 # 0.001
acc_cov_output: 500.0
gyr_cov_output: 1000.0
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.01 #0.1 # 2
imu_meas_omg_cov: 0.01 #0.1 # 2
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81.0
fov_degree: 100.0
det_range: 260.0
gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
gravity: [ 0.0, 0.0, -9.810 ] # [0.0, 9.810, 0.0] # gravity to be aligned
gravity_init: [ 0.0, 0.0, -9.810 ] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
extrinsic_T: [ 0.05512, 0.02226, -0.0297 ]
extrinsic_R: [ 1.0, 0.0, 0.0,
0.0, 1.0, 0.0,
0.0, 0.0, 1.0 ]
odometry:
publish_odometry_without_downsample: false
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: false
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.
point_lio_ros2/config/mid360.yaml
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/**:
ros__parameters:
common:
lid_topic: "/livox/lidar"
imu_topic: "/livox/imu"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme),
# the timestamp of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value
preprocess:
lidar_type: 1 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR
scan_line: 4
timestamp_unit: 3 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 0.5
mapping:
imu_en: true
start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.005 # = 1 / frequency of IMU
satu_acc: 3.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35.0 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 1.0 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.01 # 0.001
acc_cov_output: 500.0
gyr_cov_output: 1000.0
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.01 #0.1 # 2
imu_meas_omg_cov: 0.01 #0.1 # 2
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81.0
fov_degree: 360.0
det_range: 100.0
gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
gravity: [ 0.0, 0.0, -9.810 ] # [0.0, 9.810, 0.0] # gravity to be aligned
gravity_init: [ 0.0, 0.0, -9.810 ] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
extrinsic_T: [ -0.011, -0.02329, 0.04412 ]
extrinsic_R: [ 1.0, 0.0, 0.0,
0.0, 1.0, 0.0,
0.0, 0.0, 1.0 ]
odometry:
publish_odometry_without_downsample: false
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: false
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.
point_lio_ros2/config/ouster64.yaml
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/**:
ros__parameters:
common:
lid_topic: "/os_cloud_node/points"
imu_topic: "/os_cloud_node/imu"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme)
# the timestamp of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value
preprocess:
lidar_type: 3 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR
scan_line: 32 # 32 #velodyne 6 avia
timestamp_unit: 3 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 0.20
mapping:
imu_en: true
start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.01 # = 1 / frequency of IMU
satu_acc: 30.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35.0 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 9.81 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.1 # 0.01
acc_cov_output: 500.0
gyr_cov_output: 1000.0
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.1 #0.1 # 2
imu_meas_omg_cov: 0.1 #0.1 # 2
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81.0
fov_degree: 180.0
det_range: 150.0
gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
gravity: [ 0.0, 0.0, -9.810 ] # [0.0, 9.810, 0.0] # gravity to be aligned
gravity_init: [ 0.0, 0.0, -9.810 ] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
extrinsic_T: [ 0.0, 0.0, 0.0 ]
extrinsic_R: [ 1.0, 0.0, 0.0,
0.0, 1.0, 0.0,
0.0, 0.0, 1.0 ]
odometry:
publish_odometry_without_downsample: false
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: false
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.
point_lio_ros2/config/unilidar_l1.yaml
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/**:
ros__parameters:
common:
lid_topic: "/unilidar/cloud"
imu_topic: "/unilidar/imu"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme)
# the timesample of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value
preprocess:
lidar_type: 5
scan_line: 18
timestamp_unit: 0 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 0.5
mapping:
imu_en: true
start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.004 # = 1 / frequency of IMU
satu_acc: 30.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35.0 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 9.81 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.01 # 0.001
acc_cov_output: 500.0
gyr_cov_output: 1000.0
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.1 #0.1 # 2
imu_meas_omg_cov: 0.1 #0.1 # 2
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81.0
fov_degree: 180.0
det_range: 100.0
gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
gravity: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # gravity to be aligned
gravity_init: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
# transform from imu to lidar
extrinsic_T: [ 0.007698, 0.014655, -0.00667] # ulhk # [-0.5, 1.4, 1.5] # utbm
extrinsic_R: [ 1.0, 0.0, 0.0,
0.0, 1.0, 0.0,
0.0, 0.0, 1.0 ] # ulhk 4 utbm 3
odometry:
publish_odometry_without_downsample: true
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: true # save map to pcd file
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.
point_lio_ros2/config/unilidar_l2.yaml
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/**:
ros__parameters:
common:
lid_topic: "/unilidar/cloud"
imu_topic: "/unilidar/imu"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.2 # should be integral fraction of 1 / LiDAR frequency
time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme)
# the timesample of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value
preprocess:
lidar_type: 5
scan_line: 18
timestamp_unit: 0 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 1.0
mapping:
imu_en: true
start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.005 # = 1 / frequency of IMU
satu_acc: 30.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35.0 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 9.81 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.05
acc_cov_output: 500.0
gyr_cov_output: 1000.0
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.2
imu_meas_omg_cov: 0.2
gyr_cov_input: 0.05
acc_cov_input: 0.2
plane_thr: 0.2 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 50.0
fov_degree: 180.0
det_range: 20.0
gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
gravity: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # gravity to be aligned
gravity_init: [0.0, 0.0, -9.810] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
# transform from imu to lidar
extrinsic_T: [ 0.007698, 0.014655, -0.00667] # ulhk # [-0.5, 1.4, 1.5] # utbm
extrinsic_R: [ 1.0, 0.0, 0.0,
0.0, 1.0, 0.0,
0.0, 0.0, 1.0 ] # ulhk 4 utbm 3
odometry:
publish_odometry_without_downsample: false
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: true # save map to pcd file
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.
point_lio_ros2/config/velody16.yaml
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/**:
ros__parameters:
common:
lid_topic: "/velodyne_points"
imu_topic: "/imu/data"
con_frame: false # true: if you need to combine several LiDAR frames into one
con_frame_num: 1 # the number of frames combined
cut_frame: false # true: if you need to cut one LiDAR frame into several subframes
cut_frame_time_interval: 0.1 # should be integral fraction of 1 / LiDAR frequency
time_lag_imu_to_lidar: 0.0 # Time offset between LiDAR and IMU calibrated by other algorithms, e.g., LI-Init (find in Readme)
# the timestamp of IMU is transferred from the current timeline to LiDAR's timeline by subtracting this value
preprocess:
lidar_type: 2 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR
scan_line: 32
timestamp_unit: 2 # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
blind: 2.0
mapping:
imu_en: true
start_in_aggressive_motion: false # if true, a preknown gravity should be provided in following gravity_init
extrinsic_est_en: false # for aggressive motion, set this variable false
imu_time_inte: 0.01 # = 1 / frequency of IMU
satu_acc: 30.0 # the saturation value of IMU's acceleration. not related to the units
satu_gyro: 35.0 # the saturation value of IMU's angular velocity. not related to the units
acc_norm: 9.81 # 1.0 for g as unit, 9.81 for m/s^2 as unit of the IMU's acceleration
lidar_meas_cov: 0.01 # 0.001
acc_cov_output: 500.0
gyr_cov_output: 1000.0
b_acc_cov: 0.0001
b_gyr_cov: 0.0001
imu_meas_acc_cov: 0.1 #0.1 # 2
imu_meas_omg_cov: 0.1 #0.1 # 2
gyr_cov_input: 0.01 # for IMU as input model
acc_cov_input: 0.1 # for IMU as input model
plane_thr: 0.1 # 0.05, the threshold for plane criteria, the smaller, the flatter a plane
match_s: 81.0
fov_degree: 180.0
det_range: 100.0
gravity_align: true # true to align the z axis of world frame with the direction of gravity, and the gravity direction should be specified below
gravity: [ 0.0, 0.0, -9.810 ] # [0.0, 9.810, 0.0] # gravity to be aligned
gravity_init: [ 0.0, 0.0, -9.810 ] # [0.0, 9.810, 0.0] # # preknown gravity in the first IMU body frame, use when imu_en is false or start from a non-stationary state
extrinsic_T: [ 0.0, 0.0, 0.28 ] # ulhk # [-0.5, 1.4, 1.5] # utbm
# extrinsic_R: [ 0, 1, 0,
# -1, 0, 0,
# 0, 0, 1 ] # ulhk 5 6
# extrinsic_R: [ 0, -1, 0,
# 1, 0, 0,
# 0, 0, 1 ] # utbm 1, 2
extrinsic_R: [ 1.0, 0.0, 0.0,
0.0, 1.0, 0.0,
0.0, 0.0, 1.0 ] # ulhk 4 utbm 3
odometry:
publish_odometry_without_downsample: false
publish:
path_en: true # false: close the path output
scan_publish_en: true # false: close all the point cloud output
scan_bodyframe_pub_en: false # true: output the point cloud scans in IMU-body-frame
pcd_save:
pcd_save_en: false
interval: -1 # how many LiDAR frames saved in each pcd file;
# -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.
point_lio_ros2/include/FOV_Checker/FOV_Checker.cpp
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#include "FOV_Checker.h"
FOV_Checker::FOV_Checker(){
// fp = fopen("/home/ecstasy/catkin_ws/fov_data.csv","w");
// fprintf(fp,"cur_pose_x,cur_pose_y,cur_pose_z,axis_x,axis_y,axis_z,theta,depth\n");
// fclose(fp);
}
FOV_Checker::~FOV_Checker(){
}
void FOV_Checker::Set_Env(BoxPointType env_param){
env = env_param;
}
void FOV_Checker::Set_BoxLength(double box_len_param){
box_length = box_len_param;
}
void round_v3d(Eigen::Vector3d &vec, int decimal){
double tmp;
int t;
for (int i = 0; i < 3; i++){
t = pow(10,decimal);
tmp = round(vec(i)*t);
vec(i) = tmp/t;
}
return;
}
void FOV_Checker::check_fov(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, vector<BoxPointType> &boxes){
round_v3d(cur_pose,4);
round_v3d(axis,3);
axis = axis/axis.norm();
// fp = fopen("/home/ecstasy/catkin_ws/fov_data.csv","a");
// fprintf(fp,"%f,%f,%f,%f,%f,%f,%0.4f,%0.1f,",cur_pose(0),cur_pose(1),cur_pose(2),axis(0),axis(1),axis(2),theta,depth);
// fclose(fp);
// cout << "cur_pose: " << cur_pose.transpose() << endl;
// cout<< "axis: " << axis.transpose() << endl;
// cout<< "theta: " << theta << " depth: " << depth << endl;
// cout<< "env: " << env.vertex_min[0] << " " << env.vertex_max[0] << endl;
double axis_angle[6], min_angle, gap, plane_u_min, plane_u_max;
Eigen::Vector3d plane_w, plane_u, plane_v, center_point, start_point, box_p;
Eigen::Vector3d box_p_min, box_p_max;
int i, j, k, index, maxn, start_i, max_uN, max_vN, max_ulogN, u_min, u_max;
bool flag = false, box_found = false;
boxes.clear();
BoxPointType box;
axis_angle[0] = acos(axis(0));
axis_angle[1] = acos(axis(1));
axis_angle[2] = acos(axis(2));
axis_angle[3] = acos(-axis(0));
axis_angle[4] = acos(-axis(1));
axis_angle[5] = acos(-axis(2));
index = 1;
min_angle = axis_angle[0];
for (i=1;i<6;i++){
if (axis_angle[i]<min_angle){
min_angle = axis_angle[i];
index = i+1;
}
}
switch (index){
case 1:
// YZ plane
plane_w = Eigen::Vector3d(1,0,0);
plane_u = Eigen::Vector3d(0,1,0);
plane_v = Eigen::Vector3d(0,0,1);
gap = floor(cur_pose(0)/box_length + 0.5 + eps_value) * box_length + 0.5 * box_length - cur_pose(0);
maxn = ceil((env.vertex_max[0]-cur_pose(0))/box_length) +1;
start_i = 0;
break;
case 2:
// XZ plane
plane_w = Eigen::Vector3d(0,1,0);
plane_u = Eigen::Vector3d(1,0,0);
plane_v = Eigen::Vector3d(0,0,1);
gap = floor(cur_pose(1)/box_length + 0.5 + eps_value) * box_length + 0.5 * box_length - cur_pose(1);
maxn = ceil((env.vertex_max[1]-cur_pose(1))/box_length) +1;
start_i = 0;
break;
case 3:
// XY plane
plane_w = Eigen::Vector3d(0,0,1);
plane_u = Eigen::Vector3d(1,0,0);
plane_v = Eigen::Vector3d(0,1,0);
gap = floor(cur_pose(2)/box_length + 0.5 + eps_value) * box_length + 0.5 * box_length - cur_pose(2);
maxn = ceil((env.vertex_max[2]-cur_pose(2))/box_length) +1;
start_i = 0;
break;
case 4:
// YZ plane
plane_w = Eigen::Vector3d(1,0,0);
plane_u = Eigen::Vector3d(0,1,0);
plane_v = Eigen::Vector3d(0,0,1);
gap = ceil(cur_pose(0)/box_length - 0.5 - eps_value) * box_length - 0.5 * box_length - cur_pose(0);
maxn = ceil((cur_pose(0)-env.vertex_min[0])/box_length) +1;
start_i = 1;
break;
case 5:
// XZ plane
plane_w = Eigen::Vector3d(0,1,0);
plane_u = Eigen::Vector3d(1,0,0);
plane_v = Eigen::Vector3d(0,0,1);
gap = ceil(cur_pose(1)/box_length - 0.5 - eps_value) * box_length - 0.5 * box_length - cur_pose(1);
maxn = ceil((cur_pose(1)-env.vertex_min[1])/box_length) +1;
start_i = 1;
break;
case 6:
// XY plane
plane_w = Eigen::Vector3d(1,0,0);
plane_u = Eigen::Vector3d(0,1,0);
plane_v = Eigen::Vector3d(0,0,1);
gap = ceil(cur_pose(2)/box_length - 0.5 - eps_value) * box_length - 0.5 * box_length - cur_pose(2);
maxn = ceil((cur_pose(2)-env.vertex_min[2])/box_length) +1;
start_i = 1;
break;
default:
// YZ plane
plane_w = Eigen::Vector3d(1,0,0);
plane_u = Eigen::Vector3d(0,1,0);
plane_v = Eigen::Vector3d(0,0,1);
gap = ceil(cur_pose(0)/box_length + 0.5 + eps_value) * box_length + 0.5 * box_length - cur_pose(0);
maxn = ceil((env.vertex_max[0]-cur_pose(0))/box_length) +1;
start_i = 0;
break;
}
for (i=start_i; i<=maxn; i++){
center_point = cur_pose + (abs(gap) + (i-1) * box_length)/cos(min_angle) * axis;
if (index == 1 || index == 4){
start_point = Eigen::Vector3d(center_point(0),floor(center_point(1)/box_length + 0.5 + eps_value)*box_length - 0.5 * box_length,floor(center_point(2)/box_length + 0.5 + eps_value)*box_length - 0.5 * box_length);
max_uN = ceil((env.vertex_max[1]-env.vertex_min[1])/box_length);
max_ulogN = floor(log2(max_uN));
max_vN = ceil((env.vertex_max[2]-env.vertex_min[2])/box_length);
plane_u_min = env.vertex_min[1];
plane_u_max = env.vertex_max[1];
} else {
if (index == 2 || index == 5){
start_point = Eigen::Vector3d(floor(center_point(0)/box_length + 0.5 + eps_value)*box_length - 0.5 * box_length, center_point(1), floor(center_point(2)/box_length + 0.5 + eps_value)*box_length - 0.5 * box_length);
max_uN = ceil((env.vertex_max[0]-env.vertex_min[0])/box_length);
max_ulogN = floor(log2(max_uN));
max_vN = ceil((env.vertex_max[2]-env.vertex_min[2])/box_length);
plane_u_min = env.vertex_min[0];
plane_u_max = env.vertex_max[0];
} else {
start_point = Eigen::Vector3d(floor(center_point(0)/box_length + 0.5 + eps_value)*box_length - 0.5 * box_length, floor(center_point(1)/box_length + 0.5 + eps_value)*box_length - 0.5 * box_length, center_point(2));
max_uN = ceil((env.vertex_max[1]-env.vertex_min[1])/box_length);
max_ulogN = floor(log2(max_uN));
max_vN = ceil((env.vertex_max[2]-env.vertex_min[2])/box_length);
plane_u_min = env.vertex_min[1];
plane_u_max = env.vertex_max[1];
}
}
flag = false;
for (j = 1; j <= max_vN; j++){
k = max_ulogN;
u_min = 0;
box_p_min = start_point.cwiseProduct(plane_w + plane_v) + plane_u * plane_u_min + plane_v * box_length * (j-1);
box_p_max = plane_u * plane_u_max + start_point.cwiseProduct(plane_w + plane_v) + plane_v * box_length * j + plane_w * box_length;
//printf("---- UPSIDE (%0.3f,%0.3f,%0.3f),(%0.3f,%0.3f,%0.3f)\n",box_p_min[0],box_p_min[1],box_p_min[2],box_p_max[0],box_p_max[1],box_p_max[2]);
while (k>=0){
box_p = box_p_min + plane_u * box_length * (u_min + pow(2,k)) + plane_v * box_length + plane_w * box_length;
box.vertex_min[0] = box_p_min(0);
box.vertex_min[1] = box_p_min(1);
box.vertex_min[2] = box_p_min(2);
box.vertex_max[0] = box_p(0);
box.vertex_max[1] = box_p(1);
box.vertex_max[2] = box_p(2);
if (!check_box(cur_pose, axis, theta, depth, box)) u_min = u_min + pow(2,k);
k = k-1;
}
k = max_ulogN;
u_max = 0;
while (k>=0){
box_p = box_p_max - plane_u * box_length * (u_max + pow(2,k)) - plane_v * box_length - plane_w * box_length;
box.vertex_min[0] = box_p(0);
box.vertex_min[1] = box_p(1);
box.vertex_min[2] = box_p(2);
box.vertex_max[0] = box_p_max(0);
box.vertex_max[1] = box_p_max(1);
box.vertex_max[2] = box_p_max(2);
if (!check_box(cur_pose, axis, theta, depth, box)) u_max = u_max + pow(2,k);
k = k-1;
}
u_max = max(0, max_uN - u_max - 1);
box_found = false;
//printf("---- u_min -> u_max: %d->%d\n",u_min,u_max);
for (k = u_min; k <= u_max; k++){
box_p = box_p_min + plane_u * box_length * k;
box.vertex_min[0] = box_p(0);
box.vertex_min[1] = box_p(1);
box.vertex_min[2] = box_p(2);
box.vertex_max[0] = box_p(0) + box_length;
box.vertex_max[1] = box_p(1) + box_length;
box.vertex_max[2] = box_p(2) + box_length;
if (check_box_in_env(box)){
//printf("---- FOUND: (%0.3f,%0.3f,%0.3f),(%0.3f,%0.3f,%0.3f)\n",box.vertex_min[0],box.vertex_min[1],box.vertex_min[2],box.vertex_max[0],box.vertex_max[1],box.vertex_max[2]);
box_found = true;
boxes.push_back(box);
}
}
if (box_found) {
flag = true;
} else {
if (j>1) break;
}
}
for (j = 1; j <= max_vN; j++){
k = max_ulogN;
u_min = 0;
box_p_min = start_point.cwiseProduct(plane_w + plane_v) + plane_u * plane_u_min - plane_v * box_length * j;
box_p_max = plane_u * plane_u_max + start_point.cwiseProduct(plane_w + plane_v) - plane_v * box_length * (j-1) + plane_w * box_length;
//printf("---- DOWNSIDE (%0.3f,%0.3f,%0.3f),(%0.3f,%0.3f,%0.3f)\n",box_p_min[0],box_p_min[1],box_p_min[2],box_p_max[0],box_p_max[1],box_p_max[2]);
while (k>=0){
box_p = box_p_min + plane_u * box_length * (u_min + pow(2,k)) + plane_v * box_length + plane_w * box_length;
box.vertex_min[0] = box_p_min(0);
box.vertex_min[1] = box_p_min(1);
box.vertex_min[2] = box_p_min(2);
box.vertex_max[0] = box_p(0);
box.vertex_max[1] = box_p(1);
box.vertex_max[2] = box_p(2);
if (!check_box(cur_pose, axis, theta, depth, box)) u_min = u_min + pow(2,k);
k = k-1;
}
k = max_ulogN;
u_max = 0;
while (k>=0){
box_p = box_p_max - plane_u * box_length * (u_max + pow(2,k)) - plane_v * box_length - plane_w * box_length;
box.vertex_min[0] = box_p(0);
box.vertex_min[1] = box_p(1);
box.vertex_min[2] = box_p(2);
box.vertex_max[0] = box_p_max(0);
box.vertex_max[1] = box_p_max(1);
box.vertex_max[2] = box_p_max(2);
if (!check_box(cur_pose, axis, theta, depth, box)) {
u_max = u_max + pow(2,k);
// printf("-------- Not Included: (%0.3f,%0.3f,%0.3f),(%0.3f,%0.3f,%0.3f)\n",box.vertex_min[0],box.vertex_min[1],box.vertex_min[2],box.vertex_max[0],box.vertex_max[1],box.vertex_max[2]);
}
k = k-1;
}
u_max = max(0, max_uN - u_max - 1);
//printf("---- u_min -> u_max: %d->%d\n",u_min,u_max);
box_found = 0;
for (k = u_min; k <= u_max; k++){
box_p = box_p_min + plane_u * box_length * k;
box.vertex_min[0] = box_p(0);
box.vertex_min[1] = box_p(1);
box.vertex_min[2] = box_p(2);
box.vertex_max[0] = box_p(0) + box_length;
box.vertex_max[1] = box_p(1) + box_length;
box.vertex_max[2] = box_p(2) + box_length;
if (check_box_in_env(box)){
//printf("---- FOUND: (%0.3f,%0.3f,%0.3f),(%0.3f,%0.3f,%0.3f)\n",box.vertex_min[0],box.vertex_min[1],box.vertex_min[2],box.vertex_max[0],box.vertex_max[1],box.vertex_max[2]);
box_found = 1;
boxes.push_back(box);
}
}
if (box_found) {
flag = true;
} else {
if (j>1) break;
}
}
if (!flag && i>0) break;
}
}
bool FOV_Checker::check_box(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, const BoxPointType box){
Eigen::Vector3d vertex[8];
bool s;
vertex[0] = Eigen::Vector3d(box.vertex_min[0], box.vertex_min[1], box.vertex_min[2]);
vertex[1] = Eigen::Vector3d(box.vertex_min[0], box.vertex_min[1], box.vertex_max[2]);
vertex[2] = Eigen::Vector3d(box.vertex_min[0], box.vertex_max[1], box.vertex_min[2]);
vertex[3] = Eigen::Vector3d(box.vertex_min[0], box.vertex_max[1], box.vertex_max[2]);
vertex[4] = Eigen::Vector3d(box.vertex_max[0], box.vertex_min[1], box.vertex_min[2]);
vertex[5] = Eigen::Vector3d(box.vertex_max[0], box.vertex_min[1], box.vertex_max[2]);
vertex[6] = Eigen::Vector3d(box.vertex_max[0], box.vertex_max[1], box.vertex_min[2]);
vertex[7] = Eigen::Vector3d(box.vertex_max[0], box.vertex_max[1], box.vertex_max[2]);
for (int i = 0; i < 8; i++){
if (check_point(cur_pose, axis, theta, depth, vertex[i])){
return true;
}
}
Eigen::Vector3d center_point = (vertex[7]+vertex[0])/2.0;
if (check_point(cur_pose, axis, theta, depth, center_point)){
return true;
}
PlaneType plane[6];
plane[0].p[0] = vertex[0];
plane[0].p[1] = vertex[2];
plane[0].p[2] = vertex[1];
plane[0].p[3] = vertex[3];
plane[1].p[0] = vertex[0];
plane[1].p[1] = vertex[4];
plane[1].p[2] = vertex[2];
plane[1].p[3] = vertex[6];
plane[2].p[0] = vertex[0];
plane[2].p[1] = vertex[4];
plane[2].p[2] = vertex[1];
plane[2].p[3] = vertex[5];
plane[3].p[0] = vertex[4];
plane[3].p[1] = vertex[6];
plane[3].p[2] = vertex[5];
plane[3].p[3] = vertex[7];
plane[4].p[0] = vertex[2];
plane[4].p[1] = vertex[6];
plane[4].p[2] = vertex[3];
plane[4].p[3] = vertex[7];
plane[5].p[0] = vertex[1];
plane[5].p[1] = vertex[5];
plane[5].p[2] = vertex[3];
plane[5].p[3] = vertex[7];
if (check_surface(cur_pose, axis, theta, depth, plane[0]) || check_surface(cur_pose, axis, theta, depth, plane[1]) || check_surface(cur_pose, axis, theta, depth, plane[2]) || check_surface(cur_pose, axis, theta, depth, plane[3]) || check_surface(cur_pose, axis, theta, depth, plane[4]) || check_surface(cur_pose, axis, theta, depth, plane[5]))
s = 1;
else
s = 0;
return s;
}
bool FOV_Checker::check_surface(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, PlaneType plane){
Eigen::Vector3d plane_p, plane_u, plane_v, plane_w, pc, p, vec;
bool s;
double t, vec_dot_u, vec_dot_v;
plane_p = plane.p[0];
plane_u = plane.p[1] - plane_p;
plane_v = plane.p[2] - plane_p;
if (check_line(cur_pose, axis, theta, depth, plane_p, plane_u) || check_line(cur_pose, axis, theta, depth, plane_p, plane_v) || check_line(cur_pose, axis, theta, depth, plane_p + plane_u, plane_v) || check_line(cur_pose, axis, theta, depth, plane_p + plane_v, plane_u)){
s = 1;
return s;
}
pc = plane_p + (plane.p[3]-plane.p[0])/2;
if (check_point(cur_pose, axis, theta, depth, pc)){
s = 1;
return s;
}
plane_w = plane_u.cross(plane_v);
p = plane_p - cur_pose;
t = (p.dot(plane_w))/(axis.dot(plane_w));
vec = cur_pose + t * axis - plane_p;
vec_dot_u = vec.dot(plane_u)/plane_u.norm();
vec_dot_v = vec.dot(plane_v)/plane_v.norm();
if (t>=-eps_value && t<=depth && vec_dot_u>=-eps_value && vec_dot_u<=plane_u.norm() && vec_dot_v>=-eps_value && vec_dot_v <= plane_v.norm())
s = 1;
else
s = 0;
return s;
}
bool FOV_Checker::check_line(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, Eigen::Vector3d line_p, Eigen::Vector3d line_vec){
Eigen::Vector3d p, vec_1, vec_2;
double xl, yl, zl, xn, yn, zn, dot_1, dot_2, ln, pn, pl, l2, p2;
double A, B, C, delta, t1, t2;
bool s;
p = line_p - cur_pose;
xl = line_vec(0); yl = line_vec(1); zl = line_vec(2);
xn = axis(0); yn = axis(1); zn = axis(2);
vec_1 = line_p - cur_pose;
vec_2 = line_p + line_vec - cur_pose;
dot_1 = vec_1.dot(axis);
dot_2 = vec_2.dot(axis);
//printf("xl yl zl: %0.4f, %0.4f, %0.4f\n", xl, yl, zl);
//printf("xn yn zn: %0.4f, %0.4f, %0.4f\n", xn, yn, zn);
//printf("dot_1, dot_2, %0.4f, %0.4f\n",dot_1, dot_2);
if ((dot_1<0 && dot_2<0) || (dot_1>depth && dot_2>depth)){
s = false;
return s;
}
ln = xl*xn+yl*yn+zl*zn;
pn = p(0)*xn+p(1)*yn+p(2)*zn;
pl = p(0)*xl+p(1)*yl+p(2)*zl;
l2 = xl*xl+yl*yl+zl*zl;
p2 = p.norm()*p.norm();
//printf("ln: %0.4f\n",ln);
//printf("pn:%0.4f\n",pn);
//printf("pl:%0.4f\n",pl);
//printf("l2:%0.4f\n",l2);
//printf("p2:%0.4f\n",p2);
//printf("theta, cos(theta):%0.4f %0.4f\n",theta,cos(theta));
A = ln * ln - l2 * cos(theta) * cos(theta);
B = 2 * pn * ln - 2 * cos(theta) * cos(theta)*pl;
C = pn * pn - p2 * cos(theta) * cos(theta);
//printf("A:%0.4f, B:%0.4f, C:%0.4f\n", A,B,C);
if (!(fabs(A)<=eps_value)){
delta = B*B - 4*A*C;
//printf("delta: %0.4f\n",delta);
if (delta <= eps_value){
if (A < -eps_value){
s = false;
return s;
}
else{
s = true;
return s;
}
} else {
double sqrt_delta = sqrt(delta);
t1 = (-B - sqrt_delta)/(2*A);
t2 = (-B + sqrt_delta)/(2*A);
if (t1>t2) swap(t1,t2);
//printf("t1,t2: %0.4f,%0.4f\n",t1,t2);
// printf("%d\n",check_point(cur_pose, axis, theta, depth, line_p + line_vec * t1));
if ((t1>=-eps_value && t1<=1+eps_value) && check_point(cur_pose, axis, theta, depth, line_p + line_vec * t1)){
s = true;
return s;
}
// printf("%d\n",check_point(cur_pose, axis, theta, depth, line_p + line_vec * t2));
if ((t2>=-eps_value && t2<=1+eps_value) && check_point(cur_pose, axis, theta, depth, line_p + line_vec * t2)){
s = true;
return s;
}
if (A>-eps_value && (t2<eps_value || t1>1-eps_value)){
s = true;
return s;
}
if (A<eps_value && t1<eps_value && t2>1-eps_value){
s = true;
return s;
}
s = false;
}
} else{
if (!(fabs(B)<=eps_value)){
s = (B>-eps_value && -C/B<=1+eps_value) || (B<eps_value && -C/B>=-eps_value);
return s;
}
else {
s = C>=-eps_value;
return s;
}
}
return false;
}
bool FOV_Checker::check_point(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, Eigen::Vector3d point){
Eigen::Vector3d vec;
double proj_len;
bool s;
vec = point-cur_pose;
if (vec.transpose()*vec < 0.4 * box_length * box_length){
return true;
}
proj_len = vec.dot(axis);
if (proj_len > depth){
s = false;
return s;
}
//printf("acos: %0.4f\n",acos(proj_len/vec.norm()));
if (fabs(vec.norm()) <= 1e-4 || acos(proj_len/vec.norm()) <= theta + 0.0175)
s = true;
else
s = false;
return s;
}
bool FOV_Checker::check_box_in_env(BoxPointType box){
if (box.vertex_min[0] >= env.vertex_min[0]-eps_value && box.vertex_min[1] >= env.vertex_min[1]-eps_value && box.vertex_min[2] >= env.vertex_min[2]-eps_value && box.vertex_max[0]<= env.vertex_max[0]+eps_value && box.vertex_max[1]<= env.vertex_max[1]+eps_value && box.vertex_max[2]<= env.vertex_max[2]+eps_value){
return true;
} else {
return false;
}
}
point_lio_ros2/include/FOV_Checker/FOV_Checker.h
-33
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@@ -1,33 +0,0 @@
// Include Files
#pragma once
#include <math.h>
#include <cmath>
#include "ikd-Tree/ikd_Tree.h"
#include <Eigen/Core>
#include <algorithm>
#define eps_value 1e-6
struct PlaneType{
Eigen::Vector3d p[4];
};
class FOV_Checker{
public:
FOV_Checker();
~FOV_Checker();
void Set_Env(BoxPointType env_param);
void Set_BoxLength(double box_len_param);
void check_fov(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, vector<BoxPointType> &boxes);
bool check_box(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, const BoxPointType box);
bool check_line(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, Eigen::Vector3d line_p, Eigen::Vector3d line_vec);
bool check_surface(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, PlaneType plane);
bool check_point(Eigen::Vector3d cur_pose, Eigen::Vector3d axis, double theta, double depth, Eigen::Vector3d point);
bool check_box_in_env(BoxPointType box);
private:
BoxPointType env;
double box_length;
FILE *fp;
};
point_lio_ros2/include/IKFoM/.gitignore
-32
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@@ -1,32 +0,0 @@
# Prerequisites
*.d
# Compiled Object files
*.slo
*.lo
*.o
*.obj
# Precompiled Headers
*.gch
*.pch
# Compiled Dynamic libraries
*.so
*.dylib
*.dll
# Fortran module files
*.mod
*.smod
# Compiled Static libraries
*.lai
*.la
*.a
*.lib
# Executables
*.exe
*.out
*.app
point_lio_ros2/include/IKFoM/IKFoM_toolkit/esekfom/esekfom.hpp
-390
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@@ -1,390 +0,0 @@
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Author: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef ESEKFOM_EKF_HPP
#define ESEKFOM_EKF_HPP
#include <vector>
#include <cstdlib>
#include <boost/bind/bind.hpp>
#include <Eigen/Core>
#include <Eigen/Geometry>
#include <Eigen/Dense>
#include <Eigen/Eigen>
#include <Eigen/Sparse>
#include "../mtk/types/vect.hpp"
#include "../mtk/types/SOn.hpp"
#include "../mtk/types/S2.hpp"
#include "../mtk/types/SEn.hpp"
#include "../mtk/startIdx.hpp"
#include "../mtk/build_manifold.hpp"
#include "util.hpp"
namespace esekfom {
using namespace Eigen;
template<typename T>
struct dyn_share_modified
{
bool valid;
bool converge;
T M_Noise;
Eigen::Matrix<T, Eigen::Dynamic, 1> z;
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> h_x;
Eigen::Matrix<T, 6, 1> z_IMU;
Eigen::Matrix<T, 6, 1> R_IMU;
bool satu_check[6];
};
template<typename state, int process_noise_dof, typename input = state, typename measurement=state, int measurement_noise_dof=0>
class esekf{
typedef esekf self;
enum{
n = state::DOF, m = state::DIM, l = measurement::DOF
};
public:
typedef typename state::scalar scalar_type;
typedef Matrix<scalar_type, n, n> cov;
typedef Matrix<scalar_type, m, n> cov_;
typedef SparseMatrix<scalar_type> spMt;
typedef Matrix<scalar_type, n, 1> vectorized_state;
typedef Matrix<scalar_type, m, 1> flatted_state;
typedef flatted_state processModel(state &, const input &);
typedef Eigen::Matrix<scalar_type, m, n> processMatrix1(state &, const input &);
typedef Eigen::Matrix<scalar_type, m, process_noise_dof> processMatrix2(state &, const input &);
typedef Eigen::Matrix<scalar_type, process_noise_dof, process_noise_dof> processnoisecovariance;
typedef void measurementModel_dyn_share_modified(state &, dyn_share_modified<scalar_type> &);
typedef Eigen::Matrix<scalar_type ,l, n> measurementMatrix1(state &);
typedef Eigen::Matrix<scalar_type , Eigen::Dynamic, n> measurementMatrix1_dyn(state &);
typedef Eigen::Matrix<scalar_type ,l, measurement_noise_dof> measurementMatrix2(state &);
typedef Eigen::Matrix<scalar_type ,Eigen::Dynamic, Eigen::Dynamic> measurementMatrix2_dyn(state &);
typedef Eigen::Matrix<scalar_type, measurement_noise_dof, measurement_noise_dof> measurementnoisecovariance;
typedef Eigen::Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> measurementnoisecovariance_dyn;
esekf(const state &x = state(),
const cov &P = cov::Identity()): x_(x), P_(P){};
void init_dyn_share_modified(processModel f_in, processMatrix1 f_x_in, measurementModel_dyn_share_modified h_dyn_share_in)
{
f = f_in;
f_x = f_x_in;
// f_w = f_w_in;
h_dyn_share_modified_1 = h_dyn_share_in;
maximum_iter = 1;
x_.build_S2_state();
x_.build_SO3_state();
x_.build_vect_state();
x_.build_SEN_state();
}
void init_dyn_share_modified_2h(processModel f_in, processMatrix1 f_x_in, measurementModel_dyn_share_modified h_dyn_share_in1, measurementModel_dyn_share_modified h_dyn_share_in2)
{
f = f_in;
f_x = f_x_in;
// f_w = f_w_in;
h_dyn_share_modified_1 = h_dyn_share_in1;
h_dyn_share_modified_2 = h_dyn_share_in2;
maximum_iter = 1;
x_.build_S2_state();
x_.build_SO3_state();
x_.build_vect_state();
x_.build_SEN_state();
}
// iterated error state EKF propogation
void predict(double &dt, processnoisecovariance &Q, const input &i_in, bool predict_state, bool prop_cov){
if (predict_state)
{
flatted_state f_ = f(x_, i_in);
x_.oplus(f_, dt);
}
if (prop_cov)
{
flatted_state f_ = f(x_, i_in);
// state x_before = x_;
cov_ f_x_ = f_x(x_, i_in);
cov f_x_final;
F_x1 = cov::Identity();
for (std::vector<std::pair<std::pair<int, int>, int> >::iterator it = x_.vect_state.begin(); it != x_.vect_state.end(); it++) {
int idx = (*it).first.first;
int dim = (*it).first.second;
int dof = (*it).second;
for(int i = 0; i < n; i++){
for(int j=0; j<dof; j++)
{f_x_final(idx+j, i) = f_x_(dim+j, i);}
}
}
Matrix<scalar_type, 3, 3> res_temp_SO3;
MTK::vect<3, scalar_type> seg_SO3;
for (std::vector<std::pair<int, int> >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) {
int idx = (*it).first;
int dim = (*it).second;
for(int i = 0; i < 3; i++){
seg_SO3(i) = -1 * f_(dim + i) * dt;
}
MTK::SO3<scalar_type> res;
res.w() = MTK::exp<scalar_type, 3>(res.vec(), seg_SO3, scalar_type(1/2));
F_x1.template block<3, 3>(idx, idx) = res.normalized().toRotationMatrix();
res_temp_SO3 = MTK::A_matrix(seg_SO3);
for(int i = 0; i < n; i++){
f_x_final. template block<3, 1>(idx, i) = res_temp_SO3 * (f_x_. template block<3, 1>(dim, i));
}
}
F_x1 += f_x_final * dt;
P_ = F_x1 * P_ * (F_x1).transpose() + Q * (dt * dt);
}
}
bool update_iterated_dyn_share_modified() {
dyn_share_modified<scalar_type> dyn_share;
state x_propagated = x_;
int dof_Measurement;
double m_noise;
for(int i=0; i<maximum_iter; i++)
{
dyn_share.valid = true;
h_dyn_share_modified_1(x_, dyn_share);
if(! dyn_share.valid)
{
return false;
// continue;
}
Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> z = dyn_share.z;
// Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> R = dyn_share.R;
Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> h_x = dyn_share.h_x;
// Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> h_v = dyn_share.h_v;
dof_Measurement = h_x.rows();
m_noise = dyn_share.M_Noise;
// dof_Measurement_noise = dyn_share.R.rows();
// vectorized_state dx, dx_new;
// x_.boxminus(dx, x_propagated);
// dx_new = dx;
// P_ = P_propagated;
Matrix<scalar_type, n, Eigen::Dynamic> PHT;
Matrix<scalar_type, Eigen::Dynamic, Eigen::Dynamic> HPHT;
Matrix<scalar_type, n, Eigen::Dynamic> K_;
// if(n > dof_Measurement)
{
PHT = P_. template block<n, 12>(0, 0) * h_x.transpose();
HPHT = h_x * PHT.topRows(12);
for (int m = 0; m < dof_Measurement; m++)
{
HPHT(m, m) += m_noise;
}
K_= PHT*HPHT.inverse();
}
Matrix<scalar_type, n, 1> dx_ = K_ * z; // - h) + (K_x - Matrix<scalar_type, n, n>::Identity()) * dx_new;
// state x_before = x_;
x_.boxplus(dx_);
dyn_share.converge = true;
// L_ = P_;
// Matrix<scalar_type, 3, 3> res_temp_SO3;
// MTK::vect<3, scalar_type> seg_SO3;
// for(typename std::vector<std::pair<int, int> >::iterator it = x_.SO3_state.begin(); it != x_.SO3_state.end(); it++) {
// int idx = (*it).first;
// for(int i = 0; i < 3; i++){
// seg_SO3(i) = dx_(i + idx);
// }
// res_temp_SO3 = A_matrix(seg_SO3).transpose();
// for(int i = 0; i < n; i++){
// L_. template block<3, 1>(idx, i) = res_temp_SO3 * (P_. template block<3, 1>(idx, i));
// }
// {
// for(int i = 0; i < dof_Measurement; i++){
// K_. template block<3, 1>(idx, i) = res_temp_SO3 * (K_. template block<3, 1>(idx, i));
// }
// }
// for(int i = 0; i < n; i++){
// L_. template block<1, 3>(i, idx) = (L_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose();
// // P_. template block<1, 3>(i, idx) = (P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose();
// }
// for(int i = 0; i < n; i++){
// P_. template block<1, 3>(i, idx) = (P_. template block<1, 3>(i, idx)) * res_temp_SO3.transpose();
// }
// }
// Matrix<scalar_type, 2, 2> res_temp_S2;
// MTK::vect<2, scalar_type> seg_S2;
// for(typename std::vector<std::pair<int, int> >::iterator it = x_.S2_state.begin(); it != x_.S2_state.end(); it++) {
// int idx = (*it).first;
// for(int i = 0; i < 2; i++){
// seg_S2(i) = dx_(i + idx);
// }
// Eigen::Matrix<scalar_type, 2, 3> Nx;
// Eigen::Matrix<scalar_type, 3, 2> Mx;
// x_.S2_Nx_yy(Nx, idx);
// x_propagated.S2_Mx(Mx, seg_S2, idx);
// res_temp_S2 = Nx * Mx;
// for(int i = 0; i < n; i++){
// L_. template block<2, 1>(idx, i) = res_temp_S2 * (P_. template block<2, 1>(idx, i));
// }
// {
// for(int i = 0; i < dof_Measurement; i++){
// K_. template block<2, 1>(idx, i) = res_temp_S2 * (K_. template block<2, 1>(idx, i));
// }
// }
// for(int i = 0; i < n; i++){
// L_. template block<1, 2>(i, idx) = (L_. template block<1, 2>(i, idx)) * res_temp_S2.transpose();
// }
// for(int i = 0; i < n; i++){
// P_. template block<1, 2>(i, idx) = (P_. template block<1, 2>(i, idx)) * res_temp_S2.transpose();
// }
// }
// if(n > dof_Measurement)
{
P_ = P_ - K_*h_x*P_. template block<12, n>(0, 0);
}
}
return true;
}
void update_iterated_dyn_share_IMU() {
dyn_share_modified<scalar_type> dyn_share;
for(int i=0; i<maximum_iter; i++)
{
dyn_share.valid = true;
h_dyn_share_modified_2(x_, dyn_share);
Matrix<scalar_type, 6, 1> z = dyn_share.z_IMU;
Matrix<double, 30, 6> PHT;
Matrix<double, 6, 30> HP;
Matrix<double, 6, 6> HPHT;
PHT.setZero();
HP.setZero();
HPHT.setZero();
for (int l_ = 0; l_ < 6; l_++)
{
if (!dyn_share.satu_check[l_])
{
PHT.col(l_) = P_.col(15+l_) + P_.col(24+l_);
HP.row(l_) = P_.row(15+l_) + P_.row(24+l_);
}
}
for (int l_ = 0; l_ < 6; l_++)
{
if (!dyn_share.satu_check[l_])
{
HPHT.col(l_) = HP.col(15+l_) + HP.col(24+l_);
}
HPHT(l_, l_) += dyn_share.R_IMU(l_); //, l);
}
Eigen::Matrix<double, 30, 6> K = PHT * HPHT.inverse();
Matrix<scalar_type, n, 1> dx_ = K * z;
P_ -= K * HP;
x_.boxplus(dx_);
}
return;
}
void change_x(state &input_state)
{
x_ = input_state;
if((!x_.vect_state.size())&&(!x_.SO3_state.size())&&(!x_.S2_state.size())&&(!x_.SEN_state.size()))
{
x_.build_S2_state();
x_.build_SO3_state();
x_.build_vect_state();
x_.build_SEN_state();
}
}
void change_P(cov &input_cov)
{
P_ = input_cov;
}
const state& get_x() const {
return x_;
}
const cov& get_P() const {
return P_;
}
state x_;
private:
measurement m_;
cov P_;
spMt l_;
spMt f_x_1;
spMt f_x_2;
cov F_x1 = cov::Identity();
cov F_x2 = cov::Identity();
cov L_ = cov::Identity();
processModel *f;
processMatrix1 *f_x;
processMatrix2 *f_w;
measurementMatrix1 *h_x;
measurementMatrix2 *h_v;
measurementMatrix1_dyn *h_x_dyn;
measurementMatrix2_dyn *h_v_dyn;
measurementModel_dyn_share_modified *h_dyn_share_modified_1;
measurementModel_dyn_share_modified *h_dyn_share_modified_2;
int maximum_iter = 0;
scalar_type limit[n];
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
} // namespace esekfom
#endif // ESEKFOM_EKF_HPP
point_lio_ros2/include/IKFoM/IKFoM_toolkit/esekfom/util.hpp
-82
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@@ -1,82 +0,0 @@
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Author: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef __MEKFOM_UTIL_HPP__
#define __MEKFOM_UTIL_HPP__
#include <Eigen/Core>
#include "../mtk/src/mtkmath.hpp"
namespace esekfom {
template <typename T1, typename T2>
class is_same {
public:
operator bool() {
return false;
}
};
template<typename T1>
class is_same<T1, T1> {
public:
operator bool() {
return true;
}
};
template <typename T>
class is_double {
public:
operator bool() {
return false;
}
};
template<>
class is_double<double> {
public:
operator bool() {
return true;
}
};
template<typename T>
static T
id(const T &x)
{
return x;
}
} // namespace esekfom
#endif // __MEKFOM_UTIL_HPP__
point_lio_ros2/include/IKFoM/IKFoM_toolkit/mtk/build_manifold.hpp
-248
View File
@@ -1,248 +0,0 @@
// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/build_manifold.hpp
* @brief Macro to automatically construct compound manifolds.
*
*/
#ifndef MTK_AUTOCONSTRUCT_HPP_
#define MTK_AUTOCONSTRUCT_HPP_
#include <vector>
#include <boost/preprocessor/seq.hpp>
#include <boost/preprocessor/cat.hpp>
#include <Eigen/Core>
#include "src/SubManifold.hpp"
#include "startIdx.hpp"
#ifndef PARSED_BY_DOXYGEN
//////// internals //////
#define MTK_APPLY_MACRO_ON_TUPLE(r, macro, tuple) macro tuple
#define MTK_TRANSFORM_COMMA(macro, entries) BOOST_PP_SEQ_ENUM(BOOST_PP_SEQ_TRANSFORM_S(1, MTK_APPLY_MACRO_ON_TUPLE, macro, entries))
#define MTK_TRANSFORM(macro, entries) BOOST_PP_SEQ_FOR_EACH_R(1, MTK_APPLY_MACRO_ON_TUPLE, macro, entries)
#define MTK_CONSTRUCTOR_ARG( type, id) const type& id = type()
#define MTK_CONSTRUCTOR_COPY( type, id) id(id)
#define MTK_BOXPLUS( type, id) id.boxplus(MTK::subvector(__vec, &self::id), __scale);
#define MTK_OPLUS( type, id) id.oplus(MTK::subvector_(__vec, &self::id), __scale);
#define MTK_BOXMINUS( type, id) id.boxminus(MTK::subvector(__res, &self::id), __oth.id);
#define MTK_HAT( type, id) if(id.IDX == idx){id.hat(vec, res);}
#define MTK_JACOB_RIGHT_INV( type, id) if(id.IDX == idx){id.Jacob_right_inv(vec, res);}
#define MTK_JACOB_RIGHT( type, id) if(id.IDX == idx){id.Jacob_right(vec, res);}
#define MTK_S2_hat( type, id) if(id.IDX == idx){id.S2_hat(res);}
#define MTK_S2_Nx_yy( type, id) if(id.IDX == idx){id.S2_Nx_yy(res);}
#define MTK_S2_Mx( type, id) if(id.IDX == idx){id.S2_Mx(res, dx);}
#define MTK_OSTREAM( type, id) << __var.id << " "
#define MTK_ISTREAM( type, id) >> __var.id
#define MTK_S2_state( type, id) if(id.TYP == 1){S2_state.push_back(std::make_pair(id.IDX, id.DIM));}
#define MTK_SO3_state( type, id) if(id.TYP == 2){(SO3_state).push_back(std::make_pair(id.IDX, id.DIM));}
#define MTK_vect_state( type, id) if(id.TYP == 0){(vect_state).push_back(std::make_pair(std::make_pair(id.IDX, id.DIM), type::DOF));}
#define MTK_SEN_state( type, id) if(id.TYP == 4){(SEN_state).push_back(std::make_pair(std::make_pair(id.IDX, id.DIM), type::DOF));}
#define MTK_SUBVARLIST(seq, S2state, SO3state, SENstate) \
BOOST_PP_FOR_1( \
( \
BOOST_PP_SEQ_SIZE(seq), \
BOOST_PP_SEQ_HEAD(seq), \
BOOST_PP_SEQ_TAIL(seq) (~), \
0,\
0,\
S2state,\
SO3state,\
SENstate ),\
MTK_ENTRIES_TEST, MTK_ENTRIES_NEXT, MTK_ENTRIES_OUTPUT)
#define MTK_PUT_TYPE(type, id, dof, dim, S2state, SO3state, SENstate) \
MTK::SubManifold<type, dof, dim> id;
#define MTK_PUT_TYPE_AND_ENUM(type, id, dof, dim, S2state, SO3state, SENstate) \
MTK_PUT_TYPE(type, id, dof, dim, S2state, SO3state, SENstate) \
enum {DOF = type::DOF + dof}; \
enum {DIM = type::DIM+dim}; \
typedef type::scalar scalar;
#define MTK_ENTRIES_OUTPUT(r, state) MTK_ENTRIES_OUTPUT_I state
#define MTK_ENTRIES_OUTPUT_I(s, head, seq, dof, dim, S2state, SO3state, SENstate) \
MTK_APPLY_MACRO_ON_TUPLE(~, \
BOOST_PP_IF(BOOST_PP_DEC(s), MTK_PUT_TYPE, MTK_PUT_TYPE_AND_ENUM), \
( BOOST_PP_TUPLE_REM_2 head, dof, dim, S2state, SO3state, SENstate))
#define MTK_ENTRIES_TEST(r, state) MTK_TUPLE_ELEM_4_0 state
//! this used to be BOOST_PP_TUPLE_ELEM_4_0:
#define MTK_TUPLE_ELEM_4_0(a,b,c,d,e,f, g, h) a
#define MTK_ENTRIES_NEXT(r, state) MTK_ENTRIES_NEXT_I state
#define MTK_ENTRIES_NEXT_I(len, head, seq, dof, dim, S2state, SO3state, SENstate) ( \
BOOST_PP_DEC(len), \
BOOST_PP_SEQ_HEAD(seq), \
BOOST_PP_SEQ_TAIL(seq), \
dof + BOOST_PP_TUPLE_ELEM_2_0 head::DOF,\
dim + BOOST_PP_TUPLE_ELEM_2_0 head::DIM,\
S2state,\
SO3state,\
SENstate )
#endif /* not PARSED_BY_DOXYGEN */
/**
* Construct a manifold.
* @param name is the class-name of the manifold,
* @param entries is the list of sub manifolds
*
* Entries must be given in a list like this:
* @code
* typedef MTK::trafo<MTK::SO3<double> > Pose;
* typedef MTK::vect<double, 3> Vec3;
* MTK_BUILD_MANIFOLD(imu_state,
* ((Pose, pose))
* ((Vec3, vel))
* ((Vec3, acc_bias))
* )
* @endcode
* Whitespace is optional, but the double parentheses are necessary.
* Construction is done entirely in preprocessor.
* After construction @a name is also a manifold. Its members can be
* accessed by names given in @a entries.
*
* @note Variable types are not allowed to have commas, thus types like
* @c vect<double, 3> need to be typedef'ed ahead.
*/
#define MTK_BUILD_MANIFOLD(name, entries) \
struct name { \
typedef name self; \
std::vector<std::pair<int, int> > S2_state;\
std::vector<std::pair<int, int> > SO3_state;\
std::vector<std::pair<std::pair<int, int>, int> > vect_state;\
std::vector<std::pair<std::pair<int, int>, int> > SEN_state;\
MTK_SUBVARLIST(entries, S2_state, SO3_state, SEN_state) \
name ( \
MTK_TRANSFORM_COMMA(MTK_CONSTRUCTOR_ARG, entries) \
) : \
MTK_TRANSFORM_COMMA(MTK_CONSTRUCTOR_COPY, entries) {}\
int getDOF() const { return DOF; } \
void boxplus(const MTK::vectview<const scalar, DOF> & __vec, scalar __scale = 1 ) { \
MTK_TRANSFORM(MTK_BOXPLUS, entries)\
} \
void oplus(const MTK::vectview<const scalar, DIM> & __vec, scalar __scale = 1 ) { \
MTK_TRANSFORM(MTK_OPLUS, entries)\
} \
void boxminus(MTK::vectview<scalar,DOF> __res, const name& __oth) const { \
MTK_TRANSFORM(MTK_BOXMINUS, entries)\
} \
friend std::ostream& operator<<(std::ostream& __os, const name& __var){ \
return __os MTK_TRANSFORM(MTK_OSTREAM, entries); \
} \
void build_S2_state(){\
MTK_TRANSFORM(MTK_S2_state, entries)\
}\
void build_vect_state(){\
MTK_TRANSFORM(MTK_vect_state, entries)\
}\
void build_SO3_state(){\
MTK_TRANSFORM(MTK_SO3_state, entries)\
}\
void build_SEN_state(){\
MTK_TRANSFORM(MTK_SEN_state, entries)\
}\
void Lie_hat(Eigen::VectorXd &vec, Eigen::MatrixXd &res, int idx) {\
MTK_TRANSFORM(MTK_HAT, entries)\
}\
void Lie_Jacob_Right_Inv(Eigen::VectorXd &vec, Eigen::MatrixXd &res, int idx) {\
MTK_TRANSFORM(MTK_JACOB_RIGHT_INV, entries)\
}\
void Lie_Jacob_Right(Eigen::VectorXd &vec, Eigen::MatrixXd &res, int idx) {\
MTK_TRANSFORM(MTK_JACOB_RIGHT, entries)\
}\
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res, int idx) {\
MTK_TRANSFORM(MTK_S2_hat, entries)\
}\
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res, int idx) {\
MTK_TRANSFORM(MTK_S2_Nx_yy, entries)\
}\
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, Eigen::Matrix<scalar, 2, 1> dx, int idx) {\
MTK_TRANSFORM(MTK_S2_Mx, entries)\
}\
friend std::istream& operator>>(std::istream& __is, name& __var){ \
return __is MTK_TRANSFORM(MTK_ISTREAM, entries); \
} \
};
#endif /*MTK_AUTOCONSTRUCT_HPP_*/
point_lio_ros2/include/IKFoM/IKFoM_toolkit/mtk/src/SubManifold.hpp
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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/src/SubManifold.hpp
* @brief Defines the SubManifold class
*/
#ifndef SUBMANIFOLD_HPP_
#define SUBMANIFOLD_HPP_
#include "vectview.hpp"
namespace MTK {
/**
* @ingroup SubManifolds
* Helper class for compound manifolds.
* This class wraps a manifold T and provides an enum IDX refering to the
* index of the SubManifold within the compound manifold.
*
* Memberpointers to a submanifold can be used for @ref SubManifolds "functions accessing submanifolds".
*
* @tparam T The manifold type of the sub-type
* @tparam idx The index of the sub-type within the compound manifold
*/
template<class T, int idx, int dim>
struct SubManifold : public T
{
enum {IDX = idx, DIM = dim /*!< index of the sub-type within the compound manifold */ };
//! manifold type
typedef T type;
//! Construct from derived type
template<class X>
explicit
SubManifold(const X& t) : T(t) {};
//! Construct from internal type
//explicit
SubManifold(const T& t) : T(t) {};
//! inherit assignment operator
using T::operator=;
};
} // namespace MTK
#endif /* SUBMANIFOLD_HPP_ */
point_lio_ros2/include/IKFoM/IKFoM_toolkit/mtk/src/mtkmath.hpp
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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/src/mtkmath.hpp
* @brief several math utility functions.
*/
#ifndef MTKMATH_H_
#define MTKMATH_H_
#include <cmath>
#include <boost/math/tools/precision.hpp>
#include "../types/vect.hpp"
#ifndef M_PI
#define M_PI 3.1415926535897932384626433832795
#endif
namespace MTK {
namespace internal {
template<class Manifold>
struct traits {
typedef typename Manifold::scalar scalar;
enum {DOF = Manifold::DOF};
typedef vect<DOF, scalar> vectorized_type;
typedef Eigen::Matrix<scalar, DOF, DOF> matrix_type;
};
template<>
struct traits<float> : traits<Scalar<float> > {};
template<>
struct traits<double> : traits<Scalar<double> > {};
} // namespace internal
/**
* \defgroup MTKMath Mathematical helper functions
*/
//@{
//! constant @f$ \pi @f$
const double pi = M_PI;
template<class scalar> inline scalar tolerance();
template<> inline float tolerance<float >() { return 1e-5f; }
template<> inline double tolerance<double>() { return 1e-11; }
/**
* normalize @a x to @f$[-bound, bound] @f$.
*
* result for @f$ x = bound + 2\cdot n\cdot bound @f$ is arbitrary @f$\pm bound @f$.
*/
template<class scalar>
inline scalar normalize(scalar x, scalar bound){ //not used
if(std::fabs(x) <= bound) return x;
int r = (int)(x *(scalar(1.0)/ bound));
return x - ((r + (r>>31) + 1) & ~1)*bound;
}
/**
* Calculate cosine and sinc of sqrt(x2).
* @param x2 the squared angle must be non-negative
* @return a pair containing cos and sinc of sqrt(x2)
*/
template<class scalar>
std::pair<scalar, scalar> cos_sinc_sqrt(const scalar &x2){
using std::sqrt;
using std::cos;
using std::sin;
static scalar const taylor_0_bound = boost::math::tools::epsilon<scalar>();
static scalar const taylor_2_bound = sqrt(taylor_0_bound);
static scalar const taylor_n_bound = sqrt(taylor_2_bound);
assert(x2>=0 && "argument must be non-negative and must not be nan/-nan");
// FIXME check if bigger bounds are possible
if(x2>=taylor_n_bound) {
// slow fall-back solution
scalar x = sqrt(x2);
return std::make_pair(cos(x), sin(x)/x); // x is greater than 0.
}
// FIXME Replace by Horner-Scheme (4 instead of 5 FLOP/term, numerically more stable, theoretically cos and sinc can be calculated in parallel using SSE2 mulpd/addpd)
// TODO Find optimal coefficients using Remez algorithm
static scalar const inv[] = {1/3., 1/4., 1/5., 1/6., 1/7., 1/8., 1/9.};
scalar cosi = 1., sinc=1;
scalar term = -1/2. * x2;
for(int i=0; i<3; ++i) {
cosi += term;
term *= inv[2*i];
sinc += term;
term *= -inv[2*i+1] * x2;
}
return std::make_pair(cosi, sinc);
}
template<typename Base>
Eigen::Matrix<typename Base::scalar, 3, 3> hat(const Base& v) {
Eigen::Matrix<typename Base::scalar, 3, 3> res;
res << 0, -v[2], v[1],
v[2], 0, -v[0],
-v[1], v[0], 0;
return res;
}
template<typename Base>
Eigen::Matrix<typename Base::scalar, 3, 3> A_inv_trans(const Base& v){
Eigen::Matrix<typename Base::scalar, 3, 3> res;
if(v.norm() > MTK::tolerance<typename Base::scalar>())
{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity() + 0.5 * hat<Base>(v) + (1 - v.norm() * std::cos(v.norm() / 2) / 2 / std::sin(v.norm() / 2)) * hat(v) * hat(v) / v.squaredNorm();
}
else
{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity();
}
return res;
}
template<typename Base>
Eigen::Matrix<typename Base::scalar, 3, 3> A_inv(const Base& v){
Eigen::Matrix<typename Base::scalar, 3, 3> res;
if(v.norm() > MTK::tolerance<typename Base::scalar>())
{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity() - 0.5 * hat<Base>(v) + (1 - v.norm() * std::cos(v.norm() / 2) / 2 / std::sin(v.norm() / 2)) * hat(v) * hat(v) / v.squaredNorm();
}
else
{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity();
}
return res;
}
template<typename scalar>
Eigen::Matrix<scalar, 2, 3> S2_w_expw_( Eigen::Matrix<scalar, 2, 1> v, scalar length)
{
Eigen::Matrix<scalar, 2, 3> res;
scalar norm = std::sqrt(v[0]*v[0] + v[1]*v[1]);
if(norm < MTK::tolerance<scalar>()){
res = Eigen::Matrix<scalar, 2, 3>::Zero();
res(0, 1) = 1;
res(1, 2) = 1;
res /= length;
}
else{
res << -v[0]*(1/norm-1/std::tan(norm))/std::sin(norm), norm/std::sin(norm), 0,
-v[1]*(1/norm-1/std::tan(norm))/std::sin(norm), 0, norm/std::sin(norm);
res /= length;
}
}
template<typename Base>
Eigen::Matrix<typename Base::scalar, 3, 3> A_matrix(const Base & v){
Eigen::Matrix<typename Base::scalar, 3, 3> res;
double squaredNorm = v[0] * v[0] + v[1] * v[1] + v[2] * v[2];
double norm = std::sqrt(squaredNorm);
if(norm < MTK::tolerance<typename Base::scalar>()){
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity();
}
else{
res = Eigen::Matrix<typename Base::scalar, 3, 3>::Identity() + (1 - std::cos(norm)) / squaredNorm * hat(v) + (1 - std::sin(norm) / norm) / squaredNorm * hat(v) * hat(v);
}
return res;
}
template<class scalar, int n>
scalar exp(vectview<scalar, n> result, vectview<const scalar, n> vec, const scalar& scale = 1) {
scalar norm2 = vec.squaredNorm();
std::pair<scalar, scalar> cos_sinc = cos_sinc_sqrt(scale*scale * norm2);
scalar mult = cos_sinc.second * scale;
result = mult * vec;
return cos_sinc.first;
}
/**
* Inverse function to @c exp.
*
* @param result @c vectview to the result
* @param w scalar part of input
* @param vec vector part of input
* @param scale scale result by this value
* @param plus_minus_periodicity if true values @f$[w, vec]@f$ and @f$[-w, -vec]@f$ give the same result
*/
template<class scalar, int n>
void log(vectview<scalar, n> result,
const scalar &w, const vectview<const scalar, n> vec,
const scalar &scale, bool plus_minus_periodicity)
{
// FIXME implement optimized case for vec.squaredNorm() <= tolerance() * (w*w) via Rational Remez approximation ~> only one division
scalar nv = vec.norm();
if(nv < tolerance<scalar>()) {
if(!plus_minus_periodicity && w < 0) {
// find the maximal entry:
int i;
nv = vec.cwiseAbs().maxCoeff(&i);
result = scale * std::atan2(nv, w) * vect<n, scalar>::Unit(i);
return;
}
nv = tolerance<scalar>();
}
scalar s = scale / nv * (plus_minus_periodicity ? std::atan(nv / w) : std::atan2(nv, w) );
result = s * vec;
}
} // namespace MTK
#endif /* MTKMATH_H_ */
point_lio_ros2/include/IKFoM/IKFoM_toolkit/mtk/src/vectview.hpp
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/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/src/vectview.hpp
* @brief Wrapper class around a pointer used as interface for plain vectors.
*/
#ifndef VECTVIEW_HPP_
#define VECTVIEW_HPP_
#include <Eigen/Core>
namespace MTK {
/**
* A view to a vector.
* Essentially, @c vectview is only a pointer to @c scalar but can be used directly in @c Eigen expressions.
* The dimension of the vector is given as template parameter and type-checked when used in expressions.
* Data has to be modifiable.
*
* @tparam scalar Scalar type of the vector.
* @tparam dim Dimension of the vector.
*
* @todo @c vectview can be replaced by simple inheritance of @c Eigen::Map, as soon as they get const-correct
*/
namespace internal {
template<class Base, class T1, class T2>
struct CovBlock {
typedef typename Eigen::Block<Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF>, T1::DOF, T2::DOF> Type;
typedef typename Eigen::Block<const Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF>, T1::DOF, T2::DOF> ConstType;
};
template<class Base, class T1, class T2>
struct CovBlock_ {
typedef typename Eigen::Block<Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM>, T1::DIM, T2::DIM> Type;
typedef typename Eigen::Block<const Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM>, T1::DIM, T2::DIM> ConstType;
};
template<typename Base1, typename Base2, typename T1, typename T2>
struct CrossCovBlock {
typedef typename Eigen::Block<Eigen::Matrix<typename Base1::scalar, Base1::DOF, Base2::DOF>, T1::DOF, T2::DOF> Type;
typedef typename Eigen::Block<const Eigen::Matrix<typename Base1::scalar, Base1::DOF, Base2::DOF>, T1::DOF, T2::DOF> ConstType;
};
template<typename Base1, typename Base2, typename T1, typename T2>
struct CrossCovBlock_ {
typedef typename Eigen::Block<Eigen::Matrix<typename Base1::scalar, Base1::DIM, Base2::DIM>, T1::DIM, T2::DIM> Type;
typedef typename Eigen::Block<const Eigen::Matrix<typename Base1::scalar, Base1::DIM, Base2::DIM>, T1::DIM, T2::DIM> ConstType;
};
template<class scalar, int dim>
struct VectviewBase {
typedef Eigen::Matrix<scalar, dim, 1> matrix_type;
typedef typename matrix_type::MapType Type;
typedef typename matrix_type::ConstMapType ConstType;
};
template<class T>
struct UnalignedType {
typedef T type;
};
}
template<class scalar, int dim>
class vectview : public internal::VectviewBase<scalar, dim>::Type {
typedef internal::VectviewBase<scalar, dim> VectviewBase;
public:
//! plain matrix type
typedef typename VectviewBase::matrix_type matrix_type;
//! base type
typedef typename VectviewBase::Type base;
//! construct from pointer
explicit
vectview(scalar* data, int dim_=dim) : base(data, dim_) {}
//! construct from plain matrix
vectview(matrix_type& m) : base(m.data(), m.size()) {}
//! construct from another @c vectview
vectview(const vectview &v) : base(v) {}
//! construct from Eigen::Block:
template<class Base>
vectview(Eigen::VectorBlock<Base, dim> block) : base(&block.coeffRef(0), block.size()) {}
template<class Base, bool PacketAccess>
vectview(Eigen::Block<Base, dim, 1, PacketAccess> block) : base(&block.coeffRef(0), block.size()) {}
//! inherit assignment operator
using base::operator=;
//! data pointer
scalar* data() {return const_cast<scalar*>(base::data());}
};
/**
* @c const version of @c vectview.
* Compared to @c Eigen::Map this implementation is const correct, i.e.,
* data will not be modifiable using this view.
*
* @tparam scalar Scalar type of the vector.
* @tparam dim Dimension of the vector.
*
* @sa vectview
*/
template<class scalar, int dim>
class vectview<const scalar, dim> : public internal::VectviewBase<scalar, dim>::ConstType {
typedef internal::VectviewBase<scalar, dim> VectviewBase;
public:
//! plain matrix type
typedef typename VectviewBase::matrix_type matrix_type;
//! base type
typedef typename VectviewBase::ConstType base;
//! construct from const pointer
explicit
vectview(const scalar* data, int dim_ = dim) : base(data, dim_) {}
//! construct from column vector
template<int options>
vectview(const Eigen::Matrix<scalar, dim, 1, options>& m) : base(m.data()) {}
//! construct from row vector
template<int options, int phony>
vectview(const Eigen::Matrix<scalar, 1, dim, options, phony>& m) : base(m.data()) {}
//! construct from another @c vectview
vectview(vectview<scalar, dim> x) : base(x.data()) {}
//! construct from base
vectview(const base &x) : base(x) {}
/**
* Construct from Block
* @todo adapt this, when Block gets const-correct
*/
template<class Base>
vectview(Eigen::VectorBlock<Base, dim> block) : base(&block.coeffRef(0)) {}
template<class Base, bool PacketAccess>
vectview(Eigen::Block<Base, dim, 1, PacketAccess> block) : base(&block.coeffRef(0)) {}
};
} // namespace MTK
#endif /* VECTVIEW_HPP_ */
point_lio_ros2/include/IKFoM/IKFoM_toolkit/mtk/startIdx.hpp
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@@ -1,328 +0,0 @@
// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/startIdx.hpp
* @brief Tools to access sub-elements of compound manifolds.
*/
#ifndef GET_START_INDEX_H_
#define GET_START_INDEX_H_
#include <Eigen/Core>
#include "src/SubManifold.hpp"
#include "src/vectview.hpp"
namespace MTK {
/**
* \defgroup SubManifolds Accessing Submanifolds
* For compound manifolds constructed using MTK_BUILD_MANIFOLD, member pointers
* can be used to get sub-vectors or matrix-blocks of a corresponding big matrix.
* E.g. for a type @a pose consisting of @a orient and @a trans the member pointers
* @c &pose::orient and @c &pose::trans give all required information and are still
* valid if the base type gets extended or the actual types of @a orient and @a trans
* change (e.g. from 2D to 3D).
*
* @todo Maybe require manifolds to typedef MatrixType and VectorType, etc.
*/
//@{
/**
* Determine the index of a sub-variable within a compound variable.
*/
template<class Base, class T, int idx, int dim>
int getStartIdx( MTK::SubManifold<T, idx, dim> Base::*)
{
return idx;
}
template<class Base, class T, int idx, int dim>
int getStartIdx_( MTK::SubManifold<T, idx, dim> Base::*)
{
return dim;
}
/**
* Determine the degrees of freedom of a sub-variable within a compound variable.
*/
template<class Base, class T, int idx, int dim>
int getDof( MTK::SubManifold<T, idx, dim> Base::*)
{
return T::DOF;
}
template<class Base, class T, int idx, int dim>
int getDim( MTK::SubManifold<T, idx, dim> Base::*)
{
return T::DIM;
}
/**
* set the diagonal elements of a covariance matrix corresponding to a sub-variable
*/
template<class Base, class T, int idx, int dim>
void setDiagonal(Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> &cov,
MTK::SubManifold<T, idx, dim> Base::*, const typename Base::scalar &val)
{
cov.diagonal().template segment<T::DOF>(idx).setConstant(val);
}
template<class Base, class T, int idx, int dim>
void setDiagonal_(Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> &cov,
MTK::SubManifold<T, idx, dim> Base::*, const typename Base::scalar &val)
{
cov.diagonal().template segment<T::DIM>(dim).setConstant(val);
}
/**
* Get the subblock of corresponding to two members, i.e.
* \code
* Eigen::Matrix<double, Pose::DOF, Pose::DOF> m;
* MTK::subblock(m, &Pose::orient, &Pose::trans) = some_expression;
* MTK::subblock(m, &Pose::trans, &Pose::orient) = some_expression.trans();
* \endcode
* lets you modify mixed covariance entries in a bigger covariance matrix.
*/
template<class Base, class T1, int idx1, int dim1, class T2, int idx2, int dim2>
typename MTK::internal::CovBlock<Base, T1, T2>::Type
subblock(Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> &cov,
MTK::SubManifold<T1, idx1, dim1> Base::*, MTK::SubManifold<T2, idx2, dim2> Base::*)
{
return cov.template block<T1::DOF, T2::DOF>(idx1, idx2);
}
template<class Base, class T1, int idx1, int dim1, class T2, int idx2, int dim2>
typename MTK::internal::CovBlock_<Base, T1, T2>::Type
subblock_(Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> &cov,
MTK::SubManifold<T1, idx1, dim1> Base::*, MTK::SubManifold<T2, idx2, dim2> Base::*)
{
return cov.template block<T1::DIM, T2::DIM>(dim1, dim2);
}
template<typename Base1, typename Base2, typename T1, typename T2, int idx1, int idx2, int dim1, int dim2>
typename MTK::internal::CrossCovBlock<Base1, Base2, T1, T2>::Type
subblock(Eigen::Matrix<typename Base1::scalar, Base1::DOF, Base2::DOF> &cov, MTK::SubManifold<T1, idx1, dim1> Base1::*, MTK::SubManifold<T2, idx2, dim2> Base2::*)
{
return cov.template block<T1::DOF, T2::DOF>(idx1, idx2);
}
template<typename Base1, typename Base2, typename T1, typename T2, int idx1, int idx2, int dim1, int dim2>
typename MTK::internal::CrossCovBlock_<Base1, Base2, T1, T2>::Type
subblock_(Eigen::Matrix<typename Base1::scalar, Base1::DIM, Base2::DIM> &cov, MTK::SubManifold<T1, idx1, dim1> Base1::*, MTK::SubManifold<T2, idx2, dim2> Base2::*)
{
return cov.template block<T1::DIM, T2::DIM>(dim1, dim2);
}
/**
* Get the subblock of corresponding to a member, i.e.
* \code
* Eigen::Matrix<double, Pose::DOF, Pose::DOF> m;
* MTK::subblock(m, &Pose::orient) = some_expression;
* \endcode
* lets you modify covariance entries in a bigger covariance matrix.
*/
template<class Base, class T, int idx, int dim>
typename MTK::internal::CovBlock_<Base, T, T>::Type
subblock_(Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> &cov,
MTK::SubManifold<T, idx, dim> Base::*)
{
return cov.template block<T::DIM, T::DIM>(dim, dim);
}
template<class Base, class T, int idx, int dim>
typename MTK::internal::CovBlock<Base, T, T>::Type
subblock(Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> &cov,
MTK::SubManifold<T, idx, dim> Base::*)
{
return cov.template block<T::DOF, T::DOF>(idx, idx);
}
template<typename Base>
class get_cov {
public:
typedef Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> type;
typedef const Eigen::Matrix<typename Base::scalar, Base::DOF, Base::DOF> const_type;
};
template<typename Base>
class get_cov_ {
public:
typedef Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> type;
typedef const Eigen::Matrix<typename Base::scalar, Base::DIM, Base::DIM> const_type;
};
template<typename Base1, typename Base2>
class get_cross_cov {
public:
typedef Eigen::Matrix<typename Base1::scalar, Base1::DOF, Base2::DOF> type;
typedef const type const_type;
};
template<typename Base1, typename Base2>
class get_cross_cov_ {
public:
typedef Eigen::Matrix<typename Base1::scalar, Base1::DIM, Base2::DIM> type;
typedef const type const_type;
};
template<class Base, class T, int idx, int dim>
vectview<typename Base::scalar, T::DIM>
subvector_impl_(vectview<typename Base::scalar, Base::DIM> vec, SubManifold<T, idx, dim> Base::*)
{
return vec.template segment<T::DIM>(dim);
}
template<class Base, class T, int idx, int dim>
vectview<typename Base::scalar, T::DOF>
subvector_impl(vectview<typename Base::scalar, Base::DOF> vec, SubManifold<T, idx, dim> Base::*)
{
return vec.template segment<T::DOF>(idx);
}
/**
* Get the subvector corresponding to a sub-manifold from a bigger vector.
*/
template<class Scalar, int BaseDIM, class Base, class T, int idx, int dim>
vectview<Scalar, T::DIM>
subvector_(vectview<Scalar, BaseDIM> vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl_(vec, ptr);
}
template<class Scalar, int BaseDOF, class Base, class T, int idx, int dim>
vectview<Scalar, T::DOF>
subvector(vectview<Scalar, BaseDOF> vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl(vec, ptr);
}
/**
* @todo This should be covered already by subvector(vectview<typename Base::scalar,Base::DOF> vec,SubManifold<T,idx> Base::*)
*/
template<class Scalar, int BaseDOF, class Base, class T, int idx, int dim>
vectview<Scalar, T::DOF>
subvector(Eigen::Matrix<Scalar, BaseDOF, 1>& vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl(vectview<Scalar, BaseDOF>(vec), ptr);
}
template<class Scalar, int BaseDIM, class Base, class T, int idx, int dim>
vectview<Scalar, T::DIM>
subvector_(Eigen::Matrix<Scalar, BaseDIM, 1>& vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl_(vectview<Scalar, BaseDIM>(vec), ptr);
}
template<class Scalar, int BaseDIM, class Base, class T, int idx, int dim>
vectview<const Scalar, T::DIM>
subvector_(const Eigen::Matrix<Scalar, BaseDIM, 1>& vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl_(vectview<const Scalar, BaseDIM>(vec), ptr);
}
template<class Scalar, int BaseDOF, class Base, class T, int idx, int dim>
vectview<const Scalar, T::DOF>
subvector(const Eigen::Matrix<Scalar, BaseDOF, 1>& vec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl(vectview<const Scalar, BaseDOF>(vec), ptr);
}
/**
* const version of subvector(vectview<typename Base::scalar,Base::DOF> vec,SubManifold<T,idx> Base::*)
*/
template<class Base, class T, int idx, int dim>
vectview<const typename Base::scalar, T::DOF>
subvector_impl(const vectview<const typename Base::scalar, Base::DOF> cvec, SubManifold<T, idx, dim> Base::*)
{
return cvec.template segment<T::DOF>(idx);
}
template<class Base, class T, int idx, int dim>
vectview<const typename Base::scalar, T::DIM>
subvector_impl_(const vectview<const typename Base::scalar, Base::DIM> cvec, SubManifold<T, idx, dim> Base::*)
{
return cvec.template segment<T::DIM>(dim);
}
template<class Scalar, int BaseDOF, class Base, class T, int idx, int dim>
vectview<const Scalar, T::DOF>
subvector(const vectview<const Scalar, BaseDOF> cvec, SubManifold<T, idx, dim> Base::* ptr)
{
return subvector_impl(cvec, ptr);
}
} // namespace MTK
#endif // GET_START_INDEX_H_
point_lio_ros2/include/IKFoM/IKFoM_toolkit/mtk/types/S2.hpp
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@@ -1,326 +0,0 @@
// This is a NEW implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/types/S2.hpp
* @brief Unit vectors on the sphere, or directions in 3D.
*/
#ifndef S2_H_
#define S2_H_
#include "vect.hpp"
#include "SOn.hpp"
#include "../src/mtkmath.hpp"
namespace MTK {
/**
* Manifold representation of @f$ S^2 @f$.
* Used for unit vectors on the sphere or directions in 3D.
*
* @todo add conversions from/to polar angles?
*/
template<class _scalar = double, int den = 1, int num = 1, int S2_typ = 3>
struct S2 {
typedef _scalar scalar;
typedef vect<3, scalar> vect_type;
typedef SO3<scalar> SO3_type;
typedef typename vect_type::base vec3;
scalar length = scalar(den)/scalar(num);
enum {DOF=2, TYP = 1, DIM = 3};
//private:
/**
* Unit vector on the sphere, or vector pointing in a direction
*/
vect_type vec;
public:
S2() {
if(S2_typ == 3) vec=length * vec3(0, 0, std::sqrt(1));
if(S2_typ == 2) vec=length * vec3(0, std::sqrt(1), 0);
if(S2_typ == 1) vec=length * vec3(std::sqrt(1), 0, 0);
}
S2(const scalar &x, const scalar &y, const scalar &z) : vec(vec3(x, y, z)) {
vec.normalize();
vec = vec * length;
}
S2(const vect_type &_vec) : vec(_vec) {
vec.normalize();
vec = vec * length;
}
void oplus(MTK::vectview<const scalar, 3> delta, scalar scale = 1)
{
SO3_type res;
res.w() = MTK::exp<scalar, 3>(res.vec(), delta, scalar(scale/2));
vec = res.normalized().toRotationMatrix() * vec;
}
void boxplus(MTK::vectview<const scalar, 2> delta, scalar scale=1) {
Eigen::Matrix<scalar, 3, 2> Bx;
S2_Bx(Bx);
vect_type Bu = Bx*delta;SO3_type res;
res.w() = MTK::exp<scalar, 3>(res.vec(), Bu, scalar(scale/2));
vec = res.normalized().toRotationMatrix() * vec;
}
void boxminus(MTK::vectview<scalar, 2> res, const S2<scalar, den, num, S2_typ>& other) const {
scalar v_sin = (MTK::hat(vec)*other.vec).norm();
scalar v_cos = vec.transpose() * other.vec;
scalar theta = std::atan2(v_sin, v_cos);
if(v_sin < MTK::tolerance<scalar>())
{
if(std::fabs(theta) > MTK::tolerance<scalar>() )
{
res[0] = 3.1415926;
res[1] = 0;
}
else{
res[0] = 0;
res[1] = 0;
}
}
else
{
S2<scalar, den, num, S2_typ> other_copy = other;
Eigen::Matrix<scalar, 3, 2>Bx;
other_copy.S2_Bx(Bx);
res = theta/v_sin * Bx.transpose() * MTK::hat(other.vec)*vec;
}
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
Eigen::Matrix<scalar, 3, 3> skew_vec;
skew_vec << scalar(0), -vec[2], vec[1],
vec[2], scalar(0), -vec[0],
-vec[1], vec[0], scalar(0);
res = skew_vec;
}
void S2_Bx(Eigen::Matrix<scalar, 3, 2> &res)
{
if(S2_typ == 3)
{
if(vec[2] + length > tolerance<scalar>())
{
res << length - vec[0]*vec[0]/(length+vec[2]), -vec[0]*vec[1]/(length+vec[2]),
-vec[0]*vec[1]/(length+vec[2]), length-vec[1]*vec[1]/(length+vec[2]),
-vec[0], -vec[1];
res /= length;
}
else
{
res = Eigen::Matrix<scalar, 3, 2>::Zero();
res(1, 1) = -1;
res(2, 0) = 1;
}
}
else if(S2_typ == 2)
{
if(vec[1] + length > tolerance<scalar>())
{
res << length - vec[0]*vec[0]/(length+vec[1]), -vec[0]*vec[2]/(length+vec[1]),
-vec[0], -vec[2],
-vec[0]*vec[2]/(length+vec[1]), length-vec[2]*vec[2]/(length+vec[1]);
res /= length;
}
else
{
res = Eigen::Matrix<scalar, 3, 2>::Zero();
res(1, 1) = -1;
res(2, 0) = 1;
}
}
else
{
if(vec[0] + length > tolerance<scalar>())
{
res << -vec[1], -vec[2],
length - vec[1]*vec[1]/(length+vec[0]), -vec[2]*vec[1]/(length+vec[0]),
-vec[2]*vec[1]/(length+vec[0]), length-vec[2]*vec[2]/(length+vec[0]);
res /= length;
}
else
{
res = Eigen::Matrix<scalar, 3, 2>::Zero();
res(1, 1) = -1;
res(2, 0) = 1;
}
}
}
void S2_Nx(Eigen::Matrix<scalar, 2, 3> &res, S2<scalar, den, num, S2_typ>& subtrahend)
{
if((vec+subtrahend.vec).norm() > tolerance<scalar>())
{
Eigen::Matrix<scalar, 3, 2> Bx;
S2_Bx(Bx);
if((vec-subtrahend.vec).norm() > tolerance<scalar>())
{
scalar v_sin = (MTK::hat(vec)*subtrahend.vec).norm();
scalar v_cos = vec.transpose() * subtrahend.vec;
res = Bx.transpose() * (std::atan2(v_sin, v_cos)/v_sin*MTK::hat(vec)+MTK::hat(vec)*subtrahend.vec*((-v_cos/v_sin/v_sin/length/length/length/length+std::atan2(v_sin, v_cos)/v_sin/v_sin/v_sin)*subtrahend.vec.transpose()*MTK::hat(vec)*MTK::hat(vec)-vec.transpose()/length/length/length/length));
}
else
{
res = 1/length/length*Bx.transpose()*MTK::hat(vec);
}
}
else
{
std::cerr << "No N(x, y) for x=-y" << std::endl;
std::exit(100);
}
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
Eigen::Matrix<scalar, 3, 2> Bx;
S2_Bx(Bx);
res = 1/length/length*Bx.transpose()*MTK::hat(vec);
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
Eigen::Matrix<scalar, 3, 2> Bx;
S2_Bx(Bx);
if(delta.norm() < tolerance<scalar>())
{
res = -MTK::hat(vec)*Bx;
}
else{
vect_type Bu = Bx*delta;
SO3_type exp_delta;
exp_delta.w() = MTK::exp<scalar, 3>(exp_delta.vec(), Bu, scalar(1/2));
res = -exp_delta.normalized().toRotationMatrix()*MTK::hat(vec)*MTK::A_matrix(Bu).transpose()*Bx;
}
}
operator const vect_type&() const{
return vec;
}
const vect_type& get_vect() const {
return vec;
}
friend S2<scalar, den, num, S2_typ> operator*(const SO3<scalar>& rot, const S2<scalar, den, num, S2_typ>& dir)
{
S2<scalar, den, num, S2_typ> ret;
ret.vec = rot.normalized() * dir.vec;
return ret;
}
scalar operator[](int idx) const {return vec[idx]; }
friend std::ostream& operator<<(std::ostream &os, const S2<scalar, den, num, S2_typ>& vec){
return os << vec.vec.transpose() << " ";
}
friend std::istream& operator>>(std::istream &is, S2<scalar, den, num, S2_typ>& vec){
for(int i=0; i<3; ++i)
is >> vec.vec[i];
vec.vec.normalize();
vec.vec = vec.vec * vec.length;
return is;
}
};
} // namespace MTK
#endif /*S2_H_*/
point_lio_ros2/include/IKFoM/IKFoM_toolkit/mtk/types/SEn.hpp
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// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/types/SEn.hpp
* @brief Standard Orthogonal Groups i.e.\ rotatation groups.
*/
#ifndef SEN_H_
#define SEN_H_
#include <Eigen/Geometry>
#include "SOn.hpp"
#include "vect.hpp"
#include "../src/mtkmath.hpp"
namespace MTK {
/**
* Three-dimensional orientations represented as Quaternion.
* It is assumed that the internal Quaternion always stays normalized,
* should this not be the case, call inherited member function @c normalize().
*/
template<class _scalar = double, int num_of_vec_plus1 = 6, int dim_of_mat = 4, int Options = Eigen::AutoAlign>
struct SEN {
enum {DOF = num_of_vec_plus1, DIM = num_of_vec_plus1, TYP = 4};
typedef _scalar scalar;
typedef Eigen::Matrix<scalar, dim_of_mat, dim_of_mat> base;
typedef SO3<scalar> SO3_type;
// typedef Eigen::Quaternion<scalar, Options> base;
// typedef Eigen::Quaternion<scalar> Quaternion;
typedef vect<DIM, scalar, Options> vect_type;
SO3_type SO3_data;
base mat;
/**
* Construct from real part and three imaginary parts.
* Quaternion is normalized after construction.
*/
// SEN(const base& src) : mat(src) {
// // base::normalize();
// }
/**
* Construct from Eigen::Quaternion.
* @note Non-normalized input may result result in spurious behavior.
*/
SEN(const base& src = base::Identity()) : mat(src) {}
/**
* Construct from rotation matrix.
* @note Invalid rotation matrices may lead to spurious behavior.
*/
// template<class Derived>
// SO3(const Eigen::MatrixBase<Derived>& matrix) : base(matrix) {}
/**
* Construct from arbitrary rotation type.
* @note Invalid rotation matrices may lead to spurious behavior.
*/
// template<class Derived>
// SO3(const Eigen::RotationBase<Derived, 3>& rotation) : base(rotation.derived()) {}
//! @name Manifold requirements
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
SEN delta = exp(vec, scale); // ?
mat = mat * delta.mat;
}
void boxminus(MTK::vectview<scalar, DOF> res, const SEN<scalar,num_of_vec_plus1,dim_of_mat, Options>& other) const {
base error_mat = other.mat.inverse() * mat;
res = log(error_mat);
}
//}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
SEN delta = exp(vec, scale);
mat = mat * delta.mat;
}
// void hat(MTK::vectview<const scalar, DOF>& v, Eigen::Matrix<scalar, dim_of_mat, dim_of_mat> &res) {
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
res = Eigen::Matrix<scalar, dim_of_mat, dim_of_mat>::Zero();
Eigen::Matrix<scalar, 3, 3> psi;
psi << 0, -v[2], v[1],
v[2], 0, -v[0],
-v[1], v[0], 0;
res.block<3, 3>(0, 0) = psi;
for(int i = 3; i < v.size() / 3 + 2; i++)
{
res.block<3, 1>(0, i) = v.segment<3>(i + (i-3)*3);
}
// return res;
}
// void Jacob_right_inv(MTK::vectview<const scalar, DOF> vec, Eigen::Matrix<scalar, dim_of_mat, dim_of_mat> & res){
void Jacob_right_inv(Eigen::VectorXd& vec, Eigen::MatrixXd &res){
res = Eigen::Matrix<scalar, dim_of_mat, dim_of_mat>::Zero();
Eigen::Matrix<scalar, 3, 3> M_v;
Eigen::VectorXd vec_psi, vec_ro;
Eigen::MatrixXd jac_v;
Eigen::MatrixXd hat_v, hat_ro;
vec_psi = vec.segment<3>(0);
// Eigen::Matrix<scalar, 3, 1> ;
SO3_data.hat(vec_psi, hat_v);
SO3_data.Jacob_right_inv(vec_psi, jac_v);
double norm = vec_psi.norm();
for(int i = 0; i < vec.size() / 3; i++)
{
res.block<3, 3>(i*3, i*3) = jac_v;
}
for(int i = 1; i < vec.size() / 3; i++)
{
vec_ro = vec.segment<3>(i * 3);
SO3_data.hat(vec_ro, hat_ro);
if(norm > MTK::tolerance<scalar>())
{
res.block<3,3>(i*3, 0) = 0.5 * hat_ro + (1 - norm * std::cos(norm / 2) / 2 / std::sin(norm / 2))/norm/norm * (hat_ro * hat_v + hat_v * hat_ro) + ((2 - norm * std::cos(norm / 2) / 2 / std::sin(norm / 2)) / 2 / norm / norm / norm / norm - 1 / 8 / norm / norm / std::sin(norm / 2) / std::sin(norm / 2)) * hat_v * (hat_ro * hat_v + hat_v * hat_ro) * hat_v;
}
else
{
res.block<3,3>(i*3, 0) = 0.5 * hat_ro;
}
}
// return res;
}
// void Jacob_right(MTK::vectview<const scalar, DOF> & vec, Eigen::Matrix<scalar, dim_of_mat, dim_of_mat> &res){
void Jacob_right(Eigen::VectorXd& vec, Eigen::MatrixXd &res){
res = Eigen::Matrix<scalar, dim_of_mat, dim_of_mat>::Zero();
Eigen::MatrixXd hat_v, hat_ro;
Eigen::VectorXd vec_psi, vec_ro;
Eigen::MatrixXd jac_v;
vec_psi = vec.segment<3>(0);
// Eigen::Matrix<scalar, 3, 1> ;
SO3_data.hat(vec_psi, hat_v);
SO3_data.Jacob_right(vec_psi, jac_v);
// double squaredNorm = v[0] * v[0] + v[1] * v[1] + v[2] * v[2];
// double norm = std::sqrt(squaredNorm);
double norm = vec_psi.norm();
for(int i = 0; i < vec.size() / 3; i++)
{
res.block<3, 3>(i*3, i*3) = jac_v;
}
for(int i = 1; i < vec.size() / 3; i++)
{
vec_ro = vec.segment<3>(i * 3);
SO3_data.hat(vec_ro, hat_ro);
if(norm > MTK::tolerance<scalar>())
{
res.block<3,3>(i*3, 0) = -1 * jac_v * (0.5 * hat_ro + (1 - norm * std::cos(norm / 2) / 2 / std::sin(norm / 2))/norm/norm * (hat_ro * hat_v + hat_v * hat_ro) + ((2 - norm * std::cos(norm / 2) / 2 / std::sin(norm / 2)) / 2 / norm / norm / norm / norm - 1 / 8 / norm / norm / std::sin(norm / 2) / std::sin(norm / 2)) * hat_v * (hat_ro * hat_v + hat_v * hat_ro) * hat_v) * jac_v;
}
else
{
res.block<3,3>(i*3, 0) = -0.5 * jac_v * hat_ro * jac_v;
}
}
// return res;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::ostream& operator<<(std::ostream &os, const SEN<scalar, DOF, dim_of_mat, Options>& q){
for(int i=0; i<dim_of_mat; i++)
{
for(int j = 0; j < dim_of_mat; j++)
{
os << q.mat(i, j) << " ";
}
}
return os;
}
friend std::istream& operator>>(std::istream &is, SEN<scalar, DOF, dim_of_mat, Options>& q){
// vect<dim_of_mat * dim_of_mat,scalar> coeffs;
for(int i=0; i<dim_of_mat; i++)
{
for(int j = 0; j < dim_of_mat; j++)
{
is >> q.mat(i, j);
}
}
// is >> q.mat;
// coeffs;
// q.coeffs() = coeffs.normalized();
return is;
}
//! @name Helper functions
//{
/**
* Calculate the exponential map. In matrix terms this would correspond
* to the Rodrigues formula.
*/
// FIXME vectview<> can't be constructed from every MatrixBase<>, use const Vector3x& as workaround
// static SO3 exp(MTK::vectview<const scalar, 3> dvec, scalar scale = 1){
static SEN exp(const Eigen::Matrix<scalar, DOF, 1>& dvec, scalar scale = 1){
SEN res;
res.mat = Eigen::Matrix<scalar, dim_of_mat, dim_of_mat>::Identity();
Eigen::Matrix<scalar, 3, 3> exp_; //, jac;
Eigen::MatrixXd jac;
Eigen::Matrix<scalar, 3, 1> psi;
Eigen::VectorXd minus_psi;
psi = dvec.template block<3,1>(0, 0);
minus_psi = -psi;
SO3_type SO3_temp;
exp_ = SO3_type::exp(psi);
SO3_temp.Jacob_right(minus_psi, jac);
res.mat.template block<3,3>(0, 0) = exp_;
for(int i = 3; i < DOF / 3 + 2; i++)
{
res.mat.template block<3, 1>(0, i) = jac * dvec.template block<3,1>(i + (i-3)*3,0);
}
return res;
}
/**
* Calculate the inverse of @c exp.
* Only guarantees that <code>exp(log(x)) == x </code>
*/
static Eigen::Matrix<scalar, DOF, 1> log(base &orient){
Eigen::Matrix<scalar, DOF, 1> res;
Eigen::Matrix<scalar, 3, 1> psi;
Eigen::VectorXd minus_psi;
Eigen::Matrix<scalar, 3, 3> mat_psi;
Eigen::MatrixXd jac;
mat_psi = orient.template block<3, 3>(0, 0);
SO3_type SO3_temp;
SO3_type exp_psi(mat_psi);
psi = SO3_type::log(exp_psi);
minus_psi = -psi;
SO3_temp.Jacob_right_inv(minus_psi, jac);
for(int i = 3; i < dim_of_mat; i++)
{
res.template block<3,1>(i + (i-3)*3,0) = jac * orient.template block<3, 1>(0, i);
}
return res;
}
};
} // namespace MTK
#endif /*SON_H_*/
point_lio_ros2/include/IKFoM/IKFoM_toolkit/mtk/types/SOn.hpp
-365
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@@ -1,365 +0,0 @@
// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/types/SOn.hpp
* @brief Standard Orthogonal Groups i.e.\ rotatation groups.
*/
#ifndef SON_H_
#define SON_H_
#include <Eigen/Geometry>
#include "vect.hpp"
#include "../src/mtkmath.hpp"
namespace MTK {
/**
* Two-dimensional orientations represented as scalar.
* There is no guarantee that the representing scalar is within any interval,
* but the result of boxminus will always have magnitude @f$\le\pi @f$.
*/
template<class _scalar = double, int Options = Eigen::AutoAlign>
struct SO2 : public Eigen::Rotation2D<_scalar> {
enum {DOF = 1, DIM = 2, TYP = 3};
typedef _scalar scalar;
typedef Eigen::Rotation2D<scalar> base;
typedef vect<DIM, scalar, Options> vect_type;
//! Construct from angle
SO2(const scalar& angle = 0) : base(angle) { }
//! Construct from Eigen::Rotation2D
SO2(const base& src) : base(src) {}
/**
* Construct from 2D vector.
* Resulting orientation will rotate the first unit vector to point to vec.
*/
SO2(const vect_type &vec) : base(atan2(vec[1], vec[0])) {};
//! Calculate @c this->inverse() * @c r
SO2 operator%(const base &r) const {
return base::inverse() * r;
}
//! Calculate @c this->inverse() * @c r
template<class Derived>
vect_type operator%(const Eigen::MatrixBase<Derived> &vec) const {
return base::inverse() * vec;
}
//! Calculate @c *this * @c r.inverse()
SO2 operator/(const SO2 &r) const {
return *this * r.inverse();
}
//! Gets the angle as scalar.
operator scalar() const {
return base::angle();
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
//! @name Manifold requirements
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
base::angle() += scale * vec[0];
}
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
base::angle() += scale * vec[0];
}
void boxminus(MTK::vectview<scalar, DOF> res, const SO2<scalar>& other) const {
res[0] = MTK::normalize(base::angle() - other.angle(), scalar(MTK::pi));
}
friend std::istream& operator>>(std::istream &is, SO2<scalar>& ang){
return is >> ang.angle();
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
};
/**
* Three-dimensional orientations represented as Quaternion.
* It is assumed that the internal Quaternion always stays normalized,
* should this not be the case, call inherited member function @c normalize().
*/
template<class _scalar = double, int Options = Eigen::AutoAlign>
struct SO3 : public Eigen::Quaternion<_scalar, Options> {
enum {DOF = 3, DIM = 3, TYP = 2};
typedef _scalar scalar;
typedef Eigen::Quaternion<scalar, Options> base;
typedef Eigen::Quaternion<scalar> Quaternion;
typedef vect<DIM, scalar, Options> vect_type;
//! Calculate @c this->inverse() * @c r
template<class OtherDerived> EIGEN_STRONG_INLINE
Quaternion operator%(const Eigen::QuaternionBase<OtherDerived> &r) const {
return base::conjugate() * r;
}
//! Calculate @c this->inverse() * @c r
template<class Derived>
vect_type operator%(const Eigen::MatrixBase<Derived> &vec) const {
return base::conjugate() * vec;
}
//! Calculate @c this * @c r.conjugate()
template<class OtherDerived> EIGEN_STRONG_INLINE
Quaternion operator/(const Eigen::QuaternionBase<OtherDerived> &r) const {
return *this * r.conjugate();
}
/**
* Construct from real part and three imaginary parts.
* Quaternion is normalized after construction.
*/
SO3(const scalar& w, const scalar& x, const scalar& y, const scalar& z) : base(w, x, y, z) {
base::normalize();
}
/**
* Construct from Eigen::Quaternion.
* @note Non-normalized input may result result in spurious behavior.
*/
SO3(const base& src = base::Identity()) : base(src) {}
/**
* Construct from rotation matrix.
* @note Invalid rotation matrices may lead to spurious behavior.
*/
template<class Derived>
SO3(const Eigen::MatrixBase<Derived>& matrix) : base(matrix) {}
/**
* Construct from arbitrary rotation type.
* @note Invalid rotation matrices may lead to spurious behavior.
*/
template<class Derived>
SO3(const Eigen::RotationBase<Derived, 3>& rotation) : base(rotation.derived()) {}
//! @name Manifold requirements
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
SO3 delta = exp(vec, scale);
*this = *this * delta;
}
void boxminus(MTK::vectview<scalar, DOF> res, const SO3<scalar>& other) const {
res = SO3::log(other.conjugate() * *this);
}
//}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
SO3 delta = exp(vec, scale);
*this = *this * delta;
}
// void hat(MTK::vectview<const scalar, DOF>& v, Eigen::Matrix<scalar, 3, 3> &res) {
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
// Eigen::Matrix<scalar, 3, 3> res;
res << 0, -v[2], v[1],
v[2], 0, -v[0],
-v[1], v[0], 0;
// return res;
}
// void Jacob_right_inv(MTK::vectview<const scalar, DOF> vec, Eigen::Matrix<scalar, 3, 3> & res){
void Jacob_right_inv(Eigen::VectorXd& vec, Eigen::MatrixXd &res){
Eigen::MatrixXd hat_v;
hat(vec, hat_v);
if(vec.norm() > MTK::tolerance<scalar>())
{
res = Eigen::Matrix<scalar, 3, 3>::Identity() + 0.5 * hat_v + (1 - vec.norm() * std::cos(vec.norm() / 2) / 2 / std::sin(vec.norm() / 2)) * hat_v * hat_v / vec.squaredNorm();
}
else
{
res = Eigen::Matrix<scalar, 3, 3>::Identity();
}
// return res;
}
// void Jacob_right(MTK::vectview<const scalar, DOF> & v, Eigen::Matrix<scalar, 3, 3> &res){
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res){
Eigen::MatrixXd hat_v;
hat(v, hat_v);
double squaredNorm = v[0] * v[0] + v[1] * v[1] + v[2] * v[2];
double norm = std::sqrt(squaredNorm);
if(norm < MTK::tolerance<scalar>()){
res = Eigen::Matrix<scalar, 3, 3>::Identity();
}
else{
res = Eigen::Matrix<scalar, 3, 3>::Identity() - (1 - std::cos(norm)) / squaredNorm * hat_v + (1 - std::sin(norm) / norm) / squaredNorm * hat_v * hat_v;
}
// return res;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::ostream& operator<<(std::ostream &os, const SO3<scalar, Options>& q){
return os << q.coeffs().transpose() << " ";
}
friend std::istream& operator>>(std::istream &is, SO3<scalar, Options>& q){
vect<4,scalar> coeffs;
is >> coeffs;
q.coeffs() = coeffs.normalized();
return is;
}
//! @name Helper functions
//{
/**
* Calculate the exponential map. In matrix terms this would correspond
* to the Rodrigues formula.
*/
// FIXME vectview<> can't be constructed from every MatrixBase<>, use const Vector3x& as workaround
// static SO3 exp(MTK::vectview<const scalar, 3> dvec, scalar scale = 1){
static SO3 exp(const Eigen::Matrix<scalar, 3, 1>& dvec, scalar scale = 1){
SO3 res;
res.w() = MTK::exp<scalar, 3>(res.vec(), dvec, scalar(scale/2));
return res;
}
/**
* Calculate the inverse of @c exp.
* Only guarantees that <code>exp(log(x)) == x </code>
*/
static typename base::Vector3 log(const SO3 &orient){
typename base::Vector3 res;
MTK::log<scalar, 3>(res, orient.w(), orient.vec(), scalar(2), true);
return res;
}
};
namespace internal {
template<class Scalar, int Options>
struct UnalignedType<SO2<Scalar, Options > >{
typedef SO2<Scalar, Options | Eigen::DontAlign> type;
};
template<class Scalar, int Options>
struct UnalignedType<SO3<Scalar, Options > >{
typedef SO3<Scalar, Options | Eigen::DontAlign> type;
};
} // namespace internal
} // namespace MTK
#endif /*SON_H_*/
point_lio_ros2/include/IKFoM/IKFoM_toolkit/mtk/types/vect.hpp
-511
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@@ -1,511 +0,0 @@
// This is an advanced implementation of the algorithm described in the
// following paper:
// C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
// CoRR, vol. abs/1107.1119, 2011.[Online]. Available: http://arxiv.org/abs/1107.1119
/*
* Copyright (c) 2019--2023, The University of Hong Kong
* All rights reserved.
*
* Modifier: Dongjiao HE <hdj65822@connect.hku.hk>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Copyright (c) 2008--2011, Universitaet Bremen
* All rights reserved.
*
* Author: Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
/**
* @file mtk/types/vect.hpp
* @brief Basic vectors interpreted as manifolds.
*
* This file also implements a simple wrapper for matrices, for arbitrary scalars
* and for positive scalars.
*/
#ifndef VECT_H_
#define VECT_H_
#include <iosfwd>
#include <iostream>
#include <vector>
#include "../src/vectview.hpp"
namespace MTK {
static const Eigen::IOFormat IO_no_spaces(Eigen::StreamPrecision, Eigen::DontAlignCols, ",", ",", "", "", "[", "]");
/**
* A simple vector class.
* Implementation is basically a wrapper around Eigen::Matrix with manifold
* requirements added.
*/
template<int D = 3, class _scalar = double, int _Options=Eigen::AutoAlign>
struct vect : public Eigen::Matrix<_scalar, D, 1, _Options> {
typedef Eigen::Matrix<_scalar, D, 1, _Options> base;
enum {DOF = D, DIM = D, TYP = 0};
typedef _scalar scalar;
//using base::operator=;
/** Standard constructor. Sets all values to zero. */
vect(const base &src = base::Zero()) : base(src) {}
/** Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE vect(const Eigen::DenseBase<OtherDerived>& other) : base(other) {}
/** Construct from memory. */
vect(const scalar* src, int size = DOF) : base(base::Map(src, size)) { }
void boxplus(MTK::vectview<const scalar, D> vec, scalar scale=1) {
*this += scale * vec;
}
void boxminus(MTK::vectview<scalar, D> res, const vect<D, scalar>& other) const {
res = *this - other;
}
void oplus(MTK::vectview<const scalar, D> vec, scalar scale=1) {
*this += scale * vec;
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::ostream& operator<<(std::ostream &os, const vect<D, scalar, _Options>& v){
// Eigen sometimes messes with the streams flags, so output manually:
for(int i=0; i<DOF; ++i)
os << v(i) << " ";
return os;
}
friend std::istream& operator>>(std::istream &is, vect<D, scalar, _Options>& v){
char term=0;
is >> std::ws; // skip whitespace
switch(is.peek()) {
case '(': term=')'; is.ignore(1); break;
case '[': term=']'; is.ignore(1); break;
case '{': term='}'; is.ignore(1); break;
default: break;
}
if(D==Eigen::Dynamic) {
assert(term !=0 && "Dynamic vectors must be embraced");
std::vector<scalar> temp;
while(is.good() && is.peek() != term) {
scalar x;
is >> x;
temp.push_back(x);
if(is.peek()==',') is.ignore(1);
}
v = vect::Map(temp.data(), temp.size());
} else
for(int i=0; i<v.size(); ++i){
is >> v[i];
if(is.peek()==',') { // ignore commas between values
is.ignore(1);
}
}
if(term!=0) {
char x;
is >> x;
if(x!=term) {
is.setstate(is.badbit);
// assert(x==term && "start and end bracket do not match!");
}
}
return is;
}
template<int dim>
vectview<scalar, dim> tail(){
BOOST_STATIC_ASSERT(0< dim && dim <= DOF);
return base::template tail<dim>();
}
template<int dim>
vectview<const scalar, dim> tail() const{
BOOST_STATIC_ASSERT(0< dim && dim <= DOF);
return base::template tail<dim>();
}
template<int dim>
vectview<scalar, dim> head(){
BOOST_STATIC_ASSERT(0< dim && dim <= DOF);
return base::template head<dim>();
}
template<int dim>
vectview<const scalar, dim> head() const{
BOOST_STATIC_ASSERT(0< dim && dim <= DOF);
return base::template head<dim>();
}
};
/**
* A simple matrix class.
* Implementation is basically a wrapper around Eigen::Matrix with manifold
* requirements added, i.e., matrix is viewed as a plain vector for that.
*/
template<int M, int N, class _scalar = double, int _Options = Eigen::Matrix<_scalar, M, N>::Options>
struct matrix : public Eigen::Matrix<_scalar, M, N, _Options> {
typedef Eigen::Matrix<_scalar, M, N, _Options> base;
enum {DOF = M * N, TYP = 4, DIM=0};
typedef _scalar scalar;
using base::operator=;
/** Standard constructor. Sets all values to zero. */
matrix() {
base::setZero();
}
/** Constructor copying the value of the expression \a other */
template<typename OtherDerived>
EIGEN_STRONG_INLINE matrix(const Eigen::MatrixBase<OtherDerived>& other) : base(other) {}
/** Construct from memory. */
matrix(const scalar* src) : base(src) { }
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
*this += scale * base::Map(vec.data());
}
void boxminus(MTK::vectview<scalar, DOF> res, const matrix& other) const {
base::Map(res.data()) = *this - other;
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
*this += scale * base::Map(vec.data());
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::ostream& operator<<(std::ostream &os, const matrix<M, N, scalar, _Options>& mat){
for(int i=0; i<DOF; ++i){
os << mat.data()[i] << " ";
}
return os;
}
friend std::istream& operator>>(std::istream &is, matrix<M, N, scalar, _Options>& mat){
for(int i=0; i<DOF; ++i){
is >> mat.data()[i];
}
return is;
}
};// @todo What if M / N = Eigen::Dynamic?
/**
* A simple scalar type.
*/
template<class _scalar = double>
struct Scalar {
enum {DOF = 1, TYP = 5, DIM=0};
typedef _scalar scalar;
scalar value;
Scalar(const scalar& value = scalar(0)) : value(value) {}
operator const scalar&() const { return value; }
operator scalar&() { return value; }
Scalar& operator=(const scalar& val) { value = val; return *this; }
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
value += scale * vec[0];
}
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale=1) {
value += scale * vec[0];
}
void boxminus(MTK::vectview<scalar, DOF> res, const Scalar& other) const {
res[0] = *this - other;
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
};
/**
* Positive scalars.
* Boxplus is implemented using multiplication by @f$x\boxplus\delta = x\cdot\exp(\delta) @f$.
*/
template<class _scalar = double>
struct PositiveScalar {
enum {DOF = 1, TYP = 6, DIM=0};
typedef _scalar scalar;
scalar value;
PositiveScalar(const scalar& value = scalar(1)) : value(value) {
assert(value > scalar(0));
}
operator const scalar&() const { return value; }
PositiveScalar& operator=(const scalar& val) { assert(val>0); value = val; return *this; }
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
value *= std::exp(scale * vec[0]);
}
void boxminus(MTK::vectview<scalar, DOF> res, const PositiveScalar& other) const {
res[0] = std::log(*this / other);
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
value *= std::exp(scale * vec[0]);
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
friend std::istream& operator>>(std::istream &is, PositiveScalar<scalar>& s){
is >> s.value;
assert(s.value > 0);
return is;
}
};
template<class _scalar = double>
struct Complex : public std::complex<_scalar>{
enum {DOF = 2, TYP = 7, DIM=0};
typedef _scalar scalar;
typedef std::complex<scalar> Base;
Complex(const Base& value) : Base(value) {}
Complex(const scalar& re = 0.0, const scalar& im = 0.0) : Base(re, im) {}
Complex(const MTK::vectview<const scalar, 2> &in) : Base(in[0], in[1]) {}
template<class Derived>
Complex(const Eigen::DenseBase<Derived> &in) : Base(in[0], in[1]) {}
void boxplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
Base::real() += scale * vec[0];
Base::imag() += scale * vec[1];
};
void boxminus(MTK::vectview<scalar, DOF> res, const Complex& other) const {
Complex diff = *this - other;
res << diff.real(), diff.imag();
}
void S2_hat(Eigen::Matrix<scalar, 3, 3> &res)
{
res = Eigen::Matrix<scalar, 3, 3>::Zero();
}
void hat(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right_inv(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void Jacob_right(Eigen::VectorXd& v, Eigen::MatrixXd &res) {
std::cout << "wrong idx" << std::endl;
}
void oplus(MTK::vectview<const scalar, DOF> vec, scalar scale = 1) {
Base::real() += scale * vec[0];
Base::imag() += scale * vec[1];
};
void S2_Nx_yy(Eigen::Matrix<scalar, 2, 3> &res)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 2, 3>::Zero();
}
void S2_Mx(Eigen::Matrix<scalar, 3, 2> &res, MTK::vectview<const scalar, 2> delta)
{
std::cerr << "wrong idx for S2" << std::endl;
std::exit(100);
res = Eigen::Matrix<scalar, 3, 2>::Zero();
}
scalar squaredNorm() const {
return std::pow(Base::real(),2) + std::pow(Base::imag(),2);
}
const scalar& operator()(int i) const {
assert(0<=i && i<2 && "Index out of range");
return i==0 ? Base::real() : Base::imag();
}
scalar& operator()(int i){
assert(0<=i && i<2 && "Index out of range");
return i==0 ? Base::real() : Base::imag();
}
};
namespace internal {
template<int dim, class Scalar, int Options>
struct UnalignedType<vect<dim, Scalar, Options > >{
typedef vect<dim, Scalar, Options | Eigen::DontAlign> type;
};
} // namespace internal
} // namespace MTK
#endif /*VECT_H_*/
point_lio_ros2/include/IKFoM/IKFoM_toolkit/mtk/types/wrapped_cv_mat.hpp
-113
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@@ -1,113 +0,0 @@
/*
* Copyright (c) 2010--2011, Universitaet Bremen and DFKI GmbH
* All rights reserved.
*
* Author: Rene Wagner <rene.wagner@dfki.de>
* Christoph Hertzberg <chtz@informatik.uni-bremen.de>
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Universitaet Bremen nor the DFKI GmbH
* nor the names of its contributors may be used to endorse or
* promote products derived from this software without specific
* prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef WRAPPED_CV_MAT_HPP_
#define WRAPPED_CV_MAT_HPP_
#include <Eigen/Core>
#include <opencv/cv.h>
namespace MTK {
template<class f_type>
struct cv_f_type;
template<>
struct cv_f_type<double>
{
enum {value = CV_64F};
};
template<>
struct cv_f_type<float>
{
enum {value = CV_32F};
};
/**
* cv_mat wraps a CvMat around an Eigen Matrix
*/
template<int rows, int cols, class f_type = double>
class cv_mat : public matrix<rows, cols, f_type, cols==1 ? Eigen::ColMajor : Eigen::RowMajor>
{
typedef matrix<rows, cols, f_type, cols==1 ? Eigen::ColMajor : Eigen::RowMajor> base_type;
enum {type_ = cv_f_type<f_type>::value};
CvMat cv_mat_;
public:
cv_mat()
{
cv_mat_ = cvMat(rows, cols, type_, base_type::data());
}
cv_mat(const cv_mat& oth) : base_type(oth)
{
cv_mat_ = cvMat(rows, cols, type_, base_type::data());
}
template<class Derived>
cv_mat(const Eigen::MatrixBase<Derived> &value) : base_type(value)
{
cv_mat_ = cvMat(rows, cols, type_, base_type::data());
}
template<class Derived>
cv_mat& operator=(const Eigen::MatrixBase<Derived> &value)
{
base_type::operator=(value);
return *this;
}
cv_mat& operator=(const cv_mat& value)
{
base_type::operator=(value);
return *this;
}
// FIXME: Maybe overloading operator& is not a good idea ...
CvMat* operator&()
{
return &cv_mat_;
}
const CvMat* operator&() const
{
return &cv_mat_;
}
};
} // namespace MTK
#endif /* WRAPPED_CV_MAT_HPP_ */
point_lio_ros2/include/IKFoM/LICENSE
-339
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@@ -1,339 +0,0 @@
GNU GENERAL PUBLIC LICENSE
Version 2, June 1991
Copyright (C) 1989, 1991 Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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point_lio_ros2/include/IKFoM/README.md
-489
View File
@@ -1,489 +0,0 @@
## IKFoM
**IKFoM** (Iterated Kalman Filters on Manifolds) is a computationally efficient and convenient toolkit for deploying iterated Kalman filters on various robotic systems, especially systems operating on high-dimension manifold. It implements a manifold-embedding Kalman filter which separates the manifold structures from system descriptions and is able to be used by only defining the system in a canonical form and calling the respective steps accordingly. The current implementation supports the full iterated Kalman filtering for systems on manifold <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbb{R}^m\times&space;SO(3)\times\cdots\times&space;SO(3)\times\mathbb{S}^2\times\cdots\times\mathbb{S}^2" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbb{R}^m\times&space;SO(3)\times\cdots\times&space;SO(3)\times\mathbb{S}^2\times\cdots\times\mathbb{S}^2" title="\mathbb{R}^m\times SO(3)\times\cdots\times SO(3)\times\mathbb{S}^2\times\cdots\times\mathbb{S}^2" /></a> and any of its sub-manifolds, and it is extendable to other types of manifold when necessary.
**Developers**
[Dongjiao He](https://github.com/Joanna-HE)
**Our related video**: https://youtu.be/sz_ZlDkl6fA
## 1. Prerequisites
### 1.1. **Eigen && Boost**
Eigen >= 3.3.4, Follow [Eigen Installation](http://eigen.tuxfamily.org/index.php?title=Main_Page).
Boost >= 1.65.
## 2. Usage when the measurement is of constant dimension and type.
Clone the repository:
```
git clone https://github.com/hku-mars/IKFoM.git
```
1. include the necessary head file:
```
#include<esekfom/esekfom.hpp>
```
2. Select and instantiate the primitive manifolds:
```
typedef MTK::SO3<double> SO3; // scalar type of variable: double
typedef MTK::vect<3, double> vect3; // dimension of the defined Euclidean variable: 3
typedef MTK::S2<double, 98, 10, 1> S2; // length of the S2 variable: 98/10; choose e1 as the original point of rotation: 1
```
3. Build system state, input and measurement as compound manifolds which are composed of the primitive manifolds:
```
MTK_BUILD_MANIFOLD(state, // name of compound manifold: state
((vect3, pos)) // ((primitive manifold type, name of variable))
((vect3, vel))
((SO3, rot))
((vect3, bg))
((vect3, ba))
((S2, grav))
((SO3, offset_R_L_I))
((vect3, offset_T_L_I))
);
```
4. Implement the vector field <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)" title="\mathbf{f}\left(\mathbf{x}, \mathbf{u}, \mathbf{w}\right)" /></a> that is defined as <a href="https://latex.codecogs.com/svg.image?\mathbf{x}_{k&plus;1}&space;=&space;\mathbf{x}_k\oplus\Delta&space;t\mathbf{f}(\mathbf{x}_k,&space;\mathbf{u}_k,&space;\mathbf{w}_k);\hat{\mathbf{x}}_{k&plus;1}&space;=&space;\hat{\mathbf{x}}_k\oplus\Delta&space;t\mathbf{f}(\hat{\mathbf{x}}_k,&space;\mathbf{u}_k,&space;\mathbf{0})"><see here>, and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{u},&space;\mathbf{0}\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{u},&space;\mathbf{0}\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{u}, \mathbf{0}\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)}{\partial\mathbf{w}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)}{\partial\mathbf{w}}" title="\frac{\partial\mathbf{f}\left(\mathbf{x}, \mathbf{u}, \mathbf{w}\right)}{\partial\mathbf{w}}" /></a>, where w=0 could be left out:
```
Eigen::Matrix<double, state_length, 1> f(state &s, const input &i) {
Eigen::Matrix<double, state_length, 1> res = Eigen::Matrix<double, state_length, 1>::Zero();
res(0) = s.vel[0];
res(1) = s.vel[1];
res(2) = s.vel[2];
return res;
}
Eigen::Matrix<double, state_length, state_dof> df_dx(state &s, const input &i) //notice S2 has length of 3 and dimension of 2 {
Eigen::Matrix<double, state_length, state_dof> cov = Eigen::Matrix<double, state_length, state_dof>::Zero();
cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity();
return cov;
}
Eigen::Matrix<double, state_length, process_noise_dof> df_dw(state &s, const input &i) {
Eigen::Matrix<double, state_length, process_noise_dof> cov = Eigen::Matrix<double, state_length, process_noise_dof>::Zero();
cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix();
return cov;
}
```
Those functions would be called during the ekf state predict
5. Implement the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" title="\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)" /></a> and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" /></a>:
```
measurement h(state &s, bool &valid) // the iteration stops before convergence whenever the user set valid as false
{
if (condition){ valid = false;
} // other conditions could be used to stop the ekf update iteration before convergence, otherwise the iteration will not stop until the condition of convergence is satisfied.
measurement h_;
h_.position = s.pos;
return h_;
}
Eigen::Matrix<double, measurement_dof, state_dof> dh_dx(state &s) {}
Eigen::Matrix<double, measurement_dof, measurement_noise_dof> dh_dv(state &s) {}
```
Those functions would be called during the ekf state update
6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance.
(1) initial state and covariance:
```
state init_state;
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof>::cov init_P;
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof> kf(init_state,init_P);
```
(2) default state and covariance:
```
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof> kf;
```
where **process_noise_dof** is the dimension of process noise, with the type of std int, and so for **measurement_noise_dof**.
7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object:
```
double epsi[state_dof] = {0.001};
fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged
kf.init(f, df_dx, df_dw, h, dh_dx, dh_dv, Maximum_iter, epsi);
```
8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed:
```
kf.predict(dt, Q, in); // process noise covariance: Q, an Eigen matrix
```
9. Once a measurement **z** is received, an iterated update is executed:
```
kf.update_iterated(z, R); // measurement noise covariance: R, an Eigen matrix
```
*Remarks(1):*
- We also combine the output equation and its differentiation into an union function, whose usage is the same as the above steps 1-4, and steps 5-9 are shown as follows.
5. Implement the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" title="\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)" /></a> and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" /></a>:
```
measurement h_share(state &s, esekfom::share_datastruct<state, measurement, measurement_noise_dof> &share_data)
{
if(share_data.converge) {} // this value is true means iteration is converged
if(condition) share_data.valid = false; // the iteration stops before convergence when this value is false if other conditions are satified
share_data.h_x = H_x; // H_x is the result matrix of the first differentiation
share_data.h_v = H_v; // H_v is the result matrix of the second differentiation
share_data.R = R; // R is the measurement noise covariance
share_data.z = z; // z is the obtained measurement
measurement h_;
h_.position = s.pos;
return h_;
}
```
This function would be called during ekf state update, and the output function and its derivatives, the measurement and the measurement noise would be obtained from this one union function
6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance.
(1) initial state and covariance:
```
state init_state;
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof>::cov init_P;
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof> kf(init_state,init_P);
```
(2) default state and covariance:
```
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof> kf;
```
7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object:
```
double epsi[state_dof] = {0.001};
fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged
kf.init_share(f, df_dx, df_dw, h_share, Maximum_iter, epsi);
```
8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed:
```
kf.predict(dt, Q, in); // process noise covariance: Q
```
9. Once a measurement **z** is received, an iterated update is executed:
```
kf.update_iterated_share();
```
*Remarks(2):*
- The value of the state **x** and the covariance **P** are able to be changed by functions **change_x()** and **change_P()**:
```
state set_x;
kf.change_x(set_x);
esekfom::esekf<state, process_noise_dof, input, measurement, measurement_noise_dof>::cov set_P;
kf.change_P(set_P);
```
## 3. Usage when the measurement is an Eigen vector of changing dimension.
Clone the repository:
```
git clone https://github.com/hku-mars/IKFoM.git
```
1. include the necessary head file:
```
#include<esekfom/esekfom.hpp>
```
2. Select and instantiate the primitive manifolds:
```
typedef MTK::SO3<double> SO3; // scalar type of variable: double
typedef MTK::vect<3, double> vect3; // dimension of the defined Euclidean variable: 3
typedef MTK::S2<double, 98, 10, 1> S2; // length of the S2 variable: 98/10; choose e1 as the original point of rotation: 1
```
3. Build system state and input as compound manifolds which are composed of the primitive manifolds:
```
MTK_BUILD_MANIFOLD(state, // name of compound manifold: state
((vect3, pos)) // ((primitive manifold type, name of variable))
((vect3, vel))
((SO3, rot))
((vect3, bg))
((vect3, ba))
((S2, grav))
((SO3, offset_R_L_I))
((vect3, offset_T_L_I))
);
```
4. Implement the vector field <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)" title="\mathbf{f}\left(\mathbf{x}, \mathbf{u}, \mathbf{w}\right)" /></a> that is defined as <a href="https://latex.codecogs.com/svg.image?\mathbf{x}_{k&plus;1}&space;=&space;\mathbf{x}_k\oplus\Delta&space;t\mathbf{f}(\mathbf{x}_k,&space;\mathbf{u}_k,&space;\mathbf{w}_k);\hat{\mathbf{x}}_{k&plus;1}&space;=&space;\hat{\mathbf{x}}_k\oplus\Delta&space;t\mathbf{f}(\hat{\mathbf{x}}_k,&space;\mathbf{u}_k,&space;\mathbf{0})"> <see here>, and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{u},&space;\mathbf{0}\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{u},&space;\mathbf{0}\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{u}, \mathbf{0}\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)}{\partial\mathbf{w}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)}{\partial\mathbf{w}}" title="\frac{\partial\mathbf{f}\left(\mathbf{x}, \mathbf{u}, \mathbf{w}\right)}{\partial\mathbf{w}}" /></a>, where w=0 could be left out:
```
Eigen::Matrix<double, state_length, 1> f(state &s, const input &i) {
Eigen::Matrix<double, state_length, 1> res = Eigen::Matrix<double, state_length, 1>::Zero();
res(0) = s.vel[0];
res(1) = s.vel[1];
res(2) = s.vel[2];
return res;
}
Eigen::Matrix<double, state_length, state_dof> df_dx(state &s, const input &i) //notice S2 has length of 3 and dimension of 2 {
Eigen::Matrix<double, state_length, state_dof> cov = Eigen::Matrix<double, state_length, state_dof>::Zero();
cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity();
return cov;
}
Eigen::Matrix<double, state_length, process_noise_dof> df_dw(state &s, const input &i) {
Eigen::Matrix<double, state_length, process_noise_dof> cov = Eigen::Matrix<double, state_length, process_noise_dof>::Zero();
cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix();
return cov;
}
```
Those functions would be called during ekf state predict
5. Implement the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" title="\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)" /></a> and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" /></a>:
```
Eigen::Matrix<double, Eigen::Dynamic, 1> h(state &s, bool &valid) //the iteration stops before convergence when valid is false {
if (condition){ valid = false;
} // other conditions could be used to stop the ekf update iteration before convergence, otherwise the iteration will not stop until the condition of convergence is satisfied.
Eigen::Matrix<double, Eigen::Dynamic, 1> h_;
h_(0) = s.pos[0];
return h_;
}
Eigen::Matrix<double, Eigen::Dynamic, state_dof> dh_dx(state &s) {}
Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic> dh_dv(state &s) {}
```
Those functions would be called during ekf state update
6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance.
(1) initial state and covariance:
```
state init_state;
esekfom::esekf<state, process_noise_dof, input>::cov init_P;
esekfom::esekf<state, process_noise_dof, input> kf(init_state,init_P);
```
(2) default state and covariance:
```
esekfom::esekf<state, process_noise_dof, input> kf;
```
where **process_noise_dof** is the dimension of process noise, with the type of std int, and so for **measurement_noise_dof**
7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object:
```
double epsi[state_dof] = {0.001};
fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged
kf.init_dyn(f, df_dx, df_dw, h, dh_dx, dh_dv, Maximum_iter, epsi);
```
8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed:
```
kf.predict(dt, Q, in); // process noise covariance: Q, an Eigen matrix
```
9. Once a measurement **z** is received, an iterated update is executed:
```
kf.update_iterated_dyn(z, R); // measurement noise covariance: R, an Eigen matrix
```
*Remarks(1):*
- We also combine the output equation and its differentiation into an union function, whose usage is the same as the above steps 1-4, and steps 5-9 are shown as follows.
5. Implement the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" title="\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)" /></a> and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" /></a>:
```
Eigen::Matrix<double, Eigen::Dynamic, 1> h_dyn_share(state &s, esekfom::dyn_share_datastruct<double> &dyn_share_data)
{
if(dyn_share_data.converge) {} // this value is true means iteration is converged
if(condition) share_data.valid = false; // the iteration stops before convergence when this value is false if other conditions are satified
dyn_share_data.h_x = H_x; // H_x is the result matrix of the first differentiation
dyn_share_data.h_v = H_v; // H_v is the result matrix of the second differentiation
dyn_share_data.R = R; // R is the measurement noise covariance
dyn_share_data.z = z; // z is the obtained measurement
Eigen::Matrix<double, Eigen::Dynamic, 1> h_;
h_(0) = s.pos[0];
return h_;
}
This function would be called during ekf state update, and the output function and its derivatives, the measurement and the measurement noise would be obtained from this one union function
```
6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance.
(1) initial state and covariance:
```
state init_state;
esekfom::esekf<state, process_noise_dof, input>::cov init_P;
esekfom::esekf<state, process_noise_dof, input> kf(init_state,init_P);
```
(2) default state and covariance:
```
esekfom::esekf<state, process_noise_dof, input> kf;
```
7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object:
```
double epsi[state_dof] = {0.001};
fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged
kf.init_dyn_share(f, df_dx, df_dw, h_dyn_share, Maximum_iter, epsi);
```
8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed:
```
kf.predict(dt, Q, in); // process noise covariance: Q, an Eigen matrix
```
9. Once a measurement **z** is received, an iterated update is executed:
```
kf.update_iterated_dyn_share();
```
*Remarks(2):*
- The value of the state **x** and the covariance **P** are able to be changed by functions **change_x()** and **change_P()**:
```
state set_x;
kf.change_x(set_x);
esekfom::esekf<state, process_noise_dof, input>::cov set_P;
kf.change_P(set_P);
```
## 4. Usage when the measurement is a changing manifold during the run time.
Clone the repository:
```
git clone https://github.com/hku-mars/IKFoM.git
```
1. include the necessary head file:
```
#include<esekfom/esekfom.hpp>
```
2. Select and instantiate the primitive manifolds:
```
typedef MTK::SO3<double> SO3; // scalar type of variable: double
typedef MTK::vect<3, double> vect3; // dimension of the defined Euclidean variable: 3
typedef MTK::S2<double, 98, 10, 1> S2; // length of the S2 variable: 98/10; choose e1 as the original point of rotation: 1
```
3. Build system state and input as compound manifolds which are composed of the primitive manifolds:
```
MTK_BUILD_MANIFOLD(state, // name of compound manifold: state
((vect3, pos)) // ((primitive manifold type, name of variable))
((vect3, vel))
((SO3, rot))
((vect3, bg))
((vect3, ba))
((S2, grav))
((SO3, offset_R_L_I))
((vect3, offset_T_L_I))
);
```
4. Implement the vector field <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)" title="\mathbf{f}\left(\mathbf{x}, \mathbf{u}, \mathbf{w}\right)" /></a> that is defined as <a href="https://latex.codecogs.com/svg.image?\mathbf{x}_{k&plus;1}&space;=&space;\mathbf{x}_k\oplus\Delta&space;t\mathbf{f}(\mathbf{x}_k,&space;\mathbf{u}_k,&space;\mathbf{w}_k);\hat{\mathbf{x}}_{k&plus;1}&space;=&space;\hat{\mathbf{x}}_k\oplus\Delta&space;t\mathbf{f}(\hat{\mathbf{x}}_k,&space;\mathbf{u}_k,&space;\mathbf{0})"> <see here>, and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{u},&space;\mathbf{0}\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{u},&space;\mathbf{0}\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\mathbf{f}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{u}, \mathbf{0}\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)}{\partial\mathbf{w}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\mathbf{f}\left(\mathbf{x},&space;\mathbf{u},&space;\mathbf{w}\right)}{\partial\mathbf{w}}" title="\frac{\partial\mathbf{f}\left(\mathbf{x}, \mathbf{u}, \mathbf{w}\right)}{\partial\mathbf{w}}" /></a>, where w=0 could be left out:
```
Eigen::Matrix<double, state_length, 1> f(state &s, const input &i) {
Eigen::Matrix<double, state_length, 1> res = Eigen::Matrix<double, state_length, 1>::Zero();
res(0) = s.vel[0];
res(1) = s.vel[1];
res(2) = s.vel[2];
return res;
}
Eigen::Matrix<double, state_length, state_dof> df_dx(state &s, const input &i) //notice S2 has length of 3 and dimension of 2 {
Eigen::Matrix<double, state_length, state_dof> cov = Eigen::Matrix<double, state_length, state_dof>::Zero();
cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity();
return cov;
}
Eigen::Matrix<double, state_length, process_noise_dof> df_dw(state &s, const input &i) {
Eigen::Matrix<double, state_length, process_noise_dof> cov = Eigen::Matrix<double, state_length, process_noise_dof>::Zero();
cov.template block<3, 3>(12, 3) = -s.rot.toRotationMatrix();
return cov;
}
```
Those functions would be called during ekf state predict
5. Implement the differentiation of the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" /></a>:
```
Eigen::Matrix<double, Eigen::Dynamic, state_dof> dh_dx(state &s, bool &valid) {} //the iteration stops before convergence when valid is false
Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic> dh_dv(state &s, bool &valid) {}
```
Those functions would be called during ekf state update
6. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance.
(1) initial state and covariance:
```
state init_state;
esekfom::esekf<state, process_noise_dof, input>::cov init_P;
esekfom::esekf<state, process_noise_dof, input> kf(init_state,init_P);
```
(2)
```
esekfom::esekf<state, process_noise_dof, input> kf;
```
Where **process_noise_dof** is the dimension of process noise, of type of std int
7. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object:
```
double epsi[state_dof] = {0.001};
fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged
kf.init_dyn_runtime(f, df_dx, df_dw, dh_dx, dh_dv, Maximum_iter, epsi);
```
8. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed:
```
kf.predict(dt, Q, in); // process noise covariance: Q
```
9. Once a measurement **z** is received, build system measurement as compound manifolds following step 3 and implement the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" title="\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)" /></a> :
```
measurement h(state &s, bool &valid) //the iteration stops before convergence when valid is false
{
if (condition) valid = false; // the update iteration could be stopped when the condition other than convergence is satisfied
measurement h_;
h_.pos = s.pos;
return h_;
}
```
then an iterated update is executed:
```
kf.update_iterated_dyn_runtime(z, R, h); // measurement noise covariance: R, an Eigen matrix
```
*Remarks(1):*
- We also combine the output equation and its differentiation into an union function, whose usage is the same as the above steps 1-4, and steps 5-9 are shown as follows.
5. Instantiate an **esekf** object **kf** and initialize it with initial or default state and covariance.
(1) initial state and covariance:
```
state init_state;
esekfom::esekf<state, process_noise_dof, input>::cov init_P;
esekfom::esekf<state, process_noise_dof, input> kf(init_state,init_P);
```
(2) default state and covariance:
```
esekfom::esekf<state, process_noise_dof, input> kf;
```
6. Deliver the defined models, std int maximum iteration numbers **Maximum_iter**, and the std array for testing convergence **epsi** into the **esekf** object:
```
double epsi[state_dof] = {0.001};
fill(epsi, epsi+state_dof, 0.001); // if the absolute of innovation of ekf update is smaller than epso, the update iteration is converged
kf.init_dyn_runtime_share(f, df_dx, df_dw, Maximum_iter, epsi);
```
7. In the running time, once an input **in** is received with time interval **dt**, a propagation is executed:
```
kf.predict(dt, Q, in); // process noise covariance: Q. an Eigen matrix
```
8. Once a measurement **z** is received, build system measurement as compound manifolds following step 3 and implement the output equation <a href="https://www.codecogs.com/eqnedit.php?latex=\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)" title="\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)" /></a> and its differentiation <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x},&space;\mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}\boxplus\delta\mathbf{x}, \mathbf{0}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\delta\mathbf{x}}" /></a>, <a href="https://www.codecogs.com/eqnedit.php?latex=\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\frac{\partial\left(\mathbf{h}\left(\mathbf{x},&space;\mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" title="\frac{\partial\left(\mathbf{h}\left(\mathbf{x}, \mathbf{v}\right)\boxminus\mathbf{h}\left(\mathbf{x},\mathbf{0}\right)\right)}{\partial\mathbf{v}}" /></a>:
```
measurement h_dyn_runtime_share(state &s, esekfom::dyn_runtime_share_datastruct<double> &dyn_runtime_share_data)
{
if(dyn_runtime_share_data.converge) {} // this value is true means iteration is converged
if(condition) dyn_runtime_share_data.valid = false; // the iteration stops before convergence when this value is false, if conditions other than convergence is satisfied
dyn_runtime_share_data.h_x = H_x; // H_x is the result matrix of the first differentiation
dyn_runtime_share_data.h_v = H_v; // H_v is the result matrix of the second differentiation
dyn_runtime_share_data.R = R; // R is the measurement noise covariance
measurement h_;
h_.pos = s.pos;
return h_;
}
```
This function would be called during ekf state update, and the output function and its derivatives, the measurement and the measurement noise would be obtained from this one union function
then an iterated update is executed:
```
kf.update_iterated_dyn_runtime_share(z, h_dyn_runtime_share);
```
*Remarks(2):*
- The value of the state **x** and the covariance **P** are able to be changed by functions **change_x()** and **change_P()**:
```
state set_x;
kf.change_x(set_x);
esekfom::esekf<state, process_noise_dof, input>::cov set_P;
kf.change_P(set_P);
```
## 5. Run the sample
Clone the repository:
```
git clone https://github.com/hku-mars/IKFoM.git
```
In the **Samples** file folder, there is the scource code that applys the **IKFoM** on the original source code from [FAST LIO](https://github.com/hku-mars/FAST_LIO). Please follow the README.md shown in that repository excepting the step **2. Build**, which is modified as:
```
cd ~/catkin_ws/src
cp -r ~/IKFoM/Samples/FAST_LIO-stable FAST_LIO-stable
cd ..
catkin_make
source devel/setup.bash
```
## 6.Acknowledgments
Thanks for C. Hertzberg, R. Wagner, U. Frese, and L. Schroder. Integratinggeneric sensor fusion algorithms with sound state representationsthrough encapsulation of manifolds.
point_lio_ros2/include/common_lib.h
-189
View File
@@ -1,189 +0,0 @@
#ifndef COMMON_LIB_H
#define COMMON_LIB_H
#include <so3_math.h>
#include <Eigen/Eigen>
#include <pcl/point_types.h>
#include <pcl/point_cloud.h>
#include <sensor_msgs/msg/imu.hpp>
#include <nav_msgs/msg/odometry.hpp>
#include <tf2_ros/transform_broadcaster.h>
#include <deque>
using namespace std;
using namespace Eigen;
#define PI_M (3.14159265358)
#define G_m_s2 (9.81) // Gravity const in GuangDong/China
#define DIM_STATE (18) // Dimension of states (Let Dim(SO(3)) = 3)
#define DIM_PROC_N (12) // Dimension of process noise (Let Dim(SO(3)) = 3)
#define CUBE_LEN (6.0)
#define LIDAR_SP_LEN (2)
#define INIT_COV (0.0001)
#define NUM_MATCH_POINTS (5)
#define MAX_MEAS_DIM (10000)
#define VEC_FROM_ARRAY(v) v[0],v[1],v[2]
#define VEC_FROM_ARRAY_SIX(v) v[0],v[1],v[2],v[3],v[4],v[5]
#define MAT_FROM_ARRAY(v) v[0],v[1],v[2],v[3],v[4],v[5],v[6],v[7],v[8]
#define CONSTRAIN(v,min,max) ((v>min)?((v<max)?v:max):min)
#define ARRAY_FROM_EIGEN(mat) mat.data(), mat.data() + mat.rows() * mat.cols()
#define STD_VEC_FROM_EIGEN(mat) vector<decltype(mat)::Scalar> (mat.data(), mat.data() + mat.rows() * mat.cols())
#define DEBUG_FILE_DIR(name) (string(string(ROOT_DIR) + "Log/"+ name))
typedef pcl::PointXYZINormal PointType;
typedef pcl::PointXYZRGB PointTypeRGB;
typedef pcl::PointCloud<PointType> PointCloudXYZI;
typedef pcl::PointCloud<PointTypeRGB> PointCloudXYZRGB;
typedef vector<PointType, Eigen::aligned_allocator<PointType>> PointVector;
typedef Vector3d V3D;
typedef Matrix3d M3D;
typedef Vector3f V3F;
typedef Matrix3f M3F;
#define MD(a,b) Matrix<double, (a), (b)>
#define VD(a) Matrix<double, (a), 1>
#define MF(a,b) Matrix<float, (a), (b)>
#define VF(a) Matrix<float, (a), 1>
const M3D Eye3d(M3D::Identity());
const M3F Eye3f(M3F::Identity());
const V3D Zero3d(0, 0, 0);
const V3F Zero3f(0, 0, 0);
struct MeasureGroup // Lidar data and imu dates for the curent process
{
MeasureGroup()
{
lidar_beg_time = 0.0;
lidar_last_time = 0.0;
this->lidar.reset(new PointCloudXYZI());
};
double lidar_beg_time;
double lidar_last_time;
PointCloudXYZI::Ptr lidar;
deque<sensor_msgs::msg::Imu::ConstSharedPtr> imu{};
};
template <typename T>
T calc_dist(PointType p1, PointType p2){
T d = (p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y - p2.y) + (p1.z - p2.z) * (p1.z - p2.z);
return d;
}
template <typename T>
T calc_dist(Eigen::Vector3d p1, PointType p2){
T d = (p1(0) - p2.x) * (p1(0) - p2.x) + (p1(1) - p2.y) * (p1(1) - p2.y) + (p1(2) - p2.z) * (p1(2) - p2.z);
return d;
}
template<typename T>
std::vector<int> time_compressing(const PointCloudXYZI::Ptr &point_cloud)
{
int points_size = point_cloud->points.size();
int j = 0;
std::vector<int> time_seq;
// time_seq.clear();
time_seq.reserve(points_size);
for(int i = 0; i < points_size - 1; i++)
{
j++;
if (point_cloud->points[i+1].curvature > point_cloud->points[i].curvature)
{
time_seq.emplace_back(j);
j = 0;
}
}
if (j == 0)
{
time_seq.emplace_back(1);
}
else
{
time_seq.emplace_back(j+1);
}
return time_seq;
}
/* comment
plane equation: Ax + By + Cz + D = 0
convert to: A/D*x + B/D*y + C/D*z = -1
solve: A0*x0 = b0
where A0_i = [x_i, y_i, z_i], x0 = [A/D, B/D, C/D]^T, b0 = [-1, ..., -1]^T
normvec: normalized x0
*/
template<typename T>
bool esti_normvector(Matrix<T, 3, 1> &normvec, const PointVector &point, const T &threshold, const int &point_num)
{
MatrixXf A(point_num, 3);
MatrixXf b(point_num, 1);
b.setOnes();
b *= -1.0f;
for (int j = 0; j < point_num; j++)
{
A(j,0) = point[j].x;
A(j,1) = point[j].y;
A(j,2) = point[j].z;
}
normvec = A.colPivHouseholderQr().solve(b);
for (int j = 0; j < point_num; j++)
{
if (fabs(normvec(0) * point[j].x + normvec(1) * point[j].y + normvec(2) * point[j].z + 1.0f) > threshold)
{
return false;
}
}
normvec.normalize();
return true;
}
template<typename T>
bool esti_plane(Matrix<T, 4, 1> &pca_result, const PointVector &point, const T &threshold)
{
Matrix<T, NUM_MATCH_POINTS, 3> A;
Matrix<T, NUM_MATCH_POINTS, 1> b;
A.setZero();
b.setOnes();
b *= -1.0f;
for (int j = 0; j < NUM_MATCH_POINTS; j++)
{
A(j,0) = point[j].x;
A(j,1) = point[j].y;
A(j,2) = point[j].z;
}
Matrix<T, 3, 1> normvec = A.colPivHouseholderQr().solve(b);
T n = normvec.norm();
pca_result(0) = normvec(0) / n;
pca_result(1) = normvec(1) / n;
pca_result(2) = normvec(2) / n;
pca_result(3) = 1.0 / n;
for (int j = 0; j < NUM_MATCH_POINTS; j++)
{
if (fabs(pca_result(0) * point[j].x + pca_result(1) * point[j].y + pca_result(2) * point[j].z + pca_result(3)) > threshold)
{
return false;
}
}
return true;
}
inline double get_time_sec(const builtin_interfaces::msg::Time &time)
{
return rclcpp::Time(time).seconds();
}
inline rclcpp::Time get_ros_time(double timestamp)
{
int32_t sec = std::floor(timestamp);
auto nanosec_d = (timestamp - std::floor(timestamp)) * 1e9;
uint32_t nanosec = nanosec_d;
return rclcpp::Time(sec, nanosec);
}
#endif
point_lio_ros2/include/ikd-Tree/README.md
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# ikd-Tree
ikd-Tree is an incremental k-d tree for robotic applications.
point_lio_ros2/include/ikd-Tree/ikd_Tree.cpp
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point_lio_ros2/include/ikd-Tree/ikd_Tree.h
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#pragma once
#include <stdio.h>
#include <queue>
#include <pthread.h>
#include <chrono>
#include <time.h>
#include <unistd.h>
#include <math.h>
#include <algorithm>
#include <memory.h>
#include <pcl/point_types.h>
#define EPSS 1e-6
#define Minimal_Unbalanced_Tree_Size 10
#define Multi_Thread_Rebuild_Point_Num 1500
#define DOWNSAMPLE_SWITCH true
#define ForceRebuildPercentage 0.2
#define Q_LEN 1000000
using namespace std;
// typedef pcl::PointXYZINormal PointType;
// typedef vector<PointType, Eigen::aligned_allocator<PointType>> PointVector;
struct BoxPointType
{
float vertex_min[3];
float vertex_max[3];
};
enum operation_set
{
ADD_POINT,
DELETE_POINT,
DELETE_BOX,
ADD_BOX,
DOWNSAMPLE_DELETE,
PUSH_DOWN
};
enum delete_point_storage_set
{
NOT_RECORD,
DELETE_POINTS_REC,
MULTI_THREAD_REC
};
template <typename PointType>
class KD_TREE
{
// using MANUAL_Q_ = MANUAL_Q<typename PointType>;
// using PointVector = std::vector<PointType>;
// using MANUAL_Q_ = MANUAL_Q<typename PointType>;
public:
using PointVector = std::vector<PointType, Eigen::aligned_allocator<PointType>>;
using Ptr = std::shared_ptr<KD_TREE<PointType>>;
struct KD_TREE_NODE
{
PointType point;
int division_axis;
int TreeSize = 1;
int invalid_point_num = 0;
int down_del_num = 0;
bool point_deleted = false;
bool tree_deleted = false;
bool point_downsample_deleted = false;
bool tree_downsample_deleted = false;
bool need_push_down_to_left = false;
bool need_push_down_to_right = false;
bool working_flag = false;
pthread_mutex_t push_down_mutex_lock;
float node_range_x[2], node_range_y[2], node_range_z[2];
float radius_sq;
KD_TREE_NODE *left_son_ptr = nullptr;
KD_TREE_NODE *right_son_ptr = nullptr;
KD_TREE_NODE *father_ptr = nullptr;
// For paper data record
float alpha_del;
float alpha_bal;
};
struct Operation_Logger_Type
{
PointType point;
BoxPointType boxpoint;
bool tree_deleted, tree_downsample_deleted;
operation_set op;
};
// static const PointType zeroP;
struct PointType_CMP
{
PointType point;
float dist = 0.0;
PointType_CMP(PointType p = PointType(), float d = INFINITY)
{
this->point = p;
this->dist = d;
};
bool operator<(const PointType_CMP &a) const
{
if (fabs(dist - a.dist) < 1e-10)
return point.x < a.point.x;
else
return dist < a.dist;
}
};
class MANUAL_HEAP
{
public:
MANUAL_HEAP(int max_capacity = 100)
{
cap = max_capacity;
heap = new PointType_CMP[max_capacity];
heap_size = 0;
}
~MANUAL_HEAP()
{
delete[] heap;
}
void pop()
{
if (heap_size == 0)
return;
heap[0] = heap[heap_size - 1];
heap_size--;
MoveDown(0);
return;
}
PointType_CMP top()
{
return heap[0];
}
void push(PointType_CMP point)
{
if (heap_size >= cap)
return;
heap[heap_size] = point;
FloatUp(heap_size);
heap_size++;
return;
}
int size()
{
return heap_size;
}
void clear()
{
heap_size = 0;
return;
}
private:
PointType_CMP *heap;
void MoveDown(int heap_index)
{
int l = heap_index * 2 + 1;
PointType_CMP tmp = heap[heap_index];
while (l < heap_size)
{
if (l + 1 < heap_size && heap[l] < heap[l + 1])
l++;
if (tmp < heap[l])
{
heap[heap_index] = heap[l];
heap_index = l;
l = heap_index * 2 + 1;
}
else
break;
}
heap[heap_index] = tmp;
return;
}
void FloatUp(int heap_index)
{
int ancestor = (heap_index - 1) / 2;
PointType_CMP tmp = heap[heap_index];
while (heap_index > 0)
{
if (heap[ancestor] < tmp)
{
heap[heap_index] = heap[ancestor];
heap_index = ancestor;
ancestor = (heap_index - 1) / 2;
}
else
break;
}
heap[heap_index] = tmp;
return;
}
int heap_size = 0;
int cap = 0;
};
class MANUAL_Q
{
private:
int head = 0, tail = 0, counter = 0;
Operation_Logger_Type q[Q_LEN];
bool is_empty;
public:
void pop()
{
if (counter == 0)
return;
head++;
head %= Q_LEN;
counter--;
if (counter == 0)
is_empty = true;
return;
}
Operation_Logger_Type front()
{
return q[head];
}
Operation_Logger_Type back()
{
return q[tail];
}
void clear()
{
head = 0;
tail = 0;
counter = 0;
is_empty = true;
return;
}
void push(Operation_Logger_Type op)
{
q[tail] = op;
counter++;
if (is_empty)
is_empty = false;
tail++;
tail %= Q_LEN;
}
bool empty()
{
return is_empty;
}
int size()
{
return counter;
}
};
private:
// Multi-thread Tree Rebuild
bool termination_flag = false;
bool rebuild_flag = false;
pthread_t rebuild_thread;
pthread_mutex_t termination_flag_mutex_lock, rebuild_ptr_mutex_lock, working_flag_mutex, search_flag_mutex;
pthread_mutex_t rebuild_logger_mutex_lock, points_deleted_rebuild_mutex_lock;
// queue<Operation_Logger_Type> Rebuild_Logger;
MANUAL_Q Rebuild_Logger;
PointVector Rebuild_PCL_Storage;
KD_TREE_NODE **Rebuild_Ptr = nullptr;
int search_mutex_counter = 0;
static void *multi_thread_ptr(void *arg);
void multi_thread_rebuild();
void start_thread();
void stop_thread();
void run_operation(KD_TREE_NODE **root, Operation_Logger_Type operation);
// KD Tree Functions and augmented variables
int Treesize_tmp = 0, Validnum_tmp = 0;
float alpha_bal_tmp = 0.5, alpha_del_tmp = 0.0;
float delete_criterion_param = 0.5f;
float balance_criterion_param = 0.7f;
float downsample_size = 0.2f;
bool Delete_Storage_Disabled = false;
KD_TREE_NODE *STATIC_ROOT_NODE = nullptr;
PointVector Points_deleted;
PointVector Downsample_Storage;
PointVector Multithread_Points_deleted;
void InitTreeNode(KD_TREE_NODE *root);
void Test_Lock_States(KD_TREE_NODE *root);
void BuildTree(KD_TREE_NODE **root, int l, int r, PointVector &Storage);
void Rebuild(KD_TREE_NODE **root);
int Delete_by_range(KD_TREE_NODE **root, BoxPointType boxpoint, bool allow_rebuild, bool is_downsample);
void Delete_by_point(KD_TREE_NODE **root, PointType point, bool allow_rebuild);
void Add_by_point(KD_TREE_NODE **root, PointType point, bool allow_rebuild, int father_axis);
void Add_by_range(KD_TREE_NODE **root, BoxPointType boxpoint, bool allow_rebuild);
void Search(KD_TREE_NODE *root, int k_nearest, PointType point, MANUAL_HEAP &q, float max_dist); //priority_queue<PointType_CMP>
void Search_by_range(KD_TREE_NODE *root, BoxPointType boxpoint, PointVector &Storage);
void Search_by_radius(KD_TREE_NODE *root, PointType point, float radius, PointVector &Storage);
bool Criterion_Check(KD_TREE_NODE *root);
void Push_Down(KD_TREE_NODE *root);
void Update(KD_TREE_NODE *root);
void delete_tree_nodes(KD_TREE_NODE **root);
void downsample(KD_TREE_NODE **root);
bool same_point(PointType a, PointType b);
float calc_dist(PointType a, PointType b);
float calc_box_dist(KD_TREE_NODE *node, PointType point);
static bool point_cmp_x(PointType a, PointType b);
static bool point_cmp_y(PointType a, PointType b);
static bool point_cmp_z(PointType a, PointType b);
public:
KD_TREE(float delete_param = 0.5, float balance_param = 0.6, float box_length = 0.2);
~KD_TREE();
void Set_delete_criterion_param(float delete_param)
{
delete_criterion_param = delete_param;
}
void Set_balance_criterion_param(float balance_param)
{
balance_criterion_param = balance_param;
}
void set_downsample_param(float downsample_param)
{
downsample_size = downsample_param;
}
void InitializeKDTree(float delete_param = 0.5, float balance_param = 0.7, float box_length = 0.2);
int size();
int validnum();
void root_alpha(float &alpha_bal, float &alpha_del);
void Build(PointVector point_cloud);
void Nearest_Search(PointType point, int k_nearest, PointVector &Nearest_Points, vector<float> &Point_Distance, float max_dist = INFINITY);
void Box_Search(const BoxPointType &Box_of_Point, PointVector &Storage);
void Radius_Search(PointType point, const float radius, PointVector &Storage);
int Add_Points(PointVector &PointToAdd, bool downsample_on);
void Add_Point_Boxes(vector<BoxPointType> &BoxPoints);
void Delete_Points(PointVector &PointToDel);
int Delete_Point_Boxes(vector<BoxPointType> &BoxPoints);
void flatten(KD_TREE_NODE *root, PointVector &Storage, delete_point_storage_set storage_type);
void acquire_removed_points(PointVector &removed_points);
BoxPointType tree_range();
PointVector PCL_Storage;
KD_TREE_NODE *Root_Node = nullptr;
int max_queue_size = 0;
};
// template <typename PointType>
// PointType KD_TREE<PointType>::zeroP = PointType(0,0,0);
point_lio_ros2/include/so3_math.h
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#ifndef SO3_MATH_H
#define SO3_MATH_H
#include <math.h>
#include <Eigen/Core>
// #include <common_lib.h>
#define SKEW_SYM_MATRX(v) 0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0
template<typename T>
Eigen::Matrix<T, 3, 3> skew_sym_mat(const Eigen::Matrix<T, 3, 1> &v)
{
Eigen::Matrix<T, 3, 3> skew_sym_mat;
skew_sym_mat<<0.0,-v[2],v[1],v[2],0.0,-v[0],-v[1],v[0],0.0;
return skew_sym_mat;
}
template<typename T>
Eigen::Matrix<T, 3, 3> Exp(const Eigen::Matrix<T, 3, 1> &&ang)
{
T ang_norm = ang.norm();
Eigen::Matrix<T, 3, 3> Eye3 = Eigen::Matrix<T, 3, 3>::Identity();
if (ang_norm > 0.0000001)
{
Eigen::Matrix<T, 3, 1> r_axis = ang / ang_norm;
Eigen::Matrix<T, 3, 3> K;
K << SKEW_SYM_MATRX(r_axis);
/// Roderigous Tranformation
return Eye3 + std::sin(ang_norm) * K + (1.0 - std::cos(ang_norm)) * K * K;
}
else
{
return Eye3;
}
}
template<typename T, typename Ts>
Eigen::Matrix<T, 3, 3> Exp(const Eigen::Matrix<T, 3, 1> &ang_vel, const Ts &dt)
{
T ang_vel_norm = ang_vel.norm();
Eigen::Matrix<T, 3, 3> Eye3 = Eigen::Matrix<T, 3, 3>::Identity();
if (ang_vel_norm > 0.0000001)
{
Eigen::Matrix<T, 3, 1> r_axis = ang_vel / ang_vel_norm;
Eigen::Matrix<T, 3, 3> K;
K << SKEW_SYM_MATRX(r_axis);
T r_ang = ang_vel_norm * dt;
/// Roderigous Tranformation
return Eye3 + std::sin(r_ang) * K + (1.0 - std::cos(r_ang)) * K * K;
}
else
{
return Eye3;
}
}
template<typename T>
Eigen::Matrix<T, 3, 3> Exp(const T &v1, const T &v2, const T &v3)
{
T &&norm = sqrt(v1 * v1 + v2 * v2 + v3 * v3);
Eigen::Matrix<T, 3, 3> Eye3 = Eigen::Matrix<T, 3, 3>::Identity();
if (norm > 0.00001)
{
T r_ang[3] = {v1 / norm, v2 / norm, v3 / norm};
Eigen::Matrix<T, 3, 3> K;
K << SKEW_SYM_MATRX(r_ang);
/// Roderigous Tranformation
return Eye3 + std::sin(norm) * K + (1.0 - std::cos(norm)) * K * K;
}
else
{
return Eye3;
}
}
/* Logrithm of a Rotation Matrix */
template<typename T>
Eigen::Matrix<T,3,1> Log(const Eigen::Matrix<T, 3, 3> &R)
{
T theta = (R.trace() > 3.0 - 1e-6) ? 0.0 : std::acos(0.5 * (R.trace() - 1));
Eigen::Matrix<T,3,1> K(R(2,1) - R(1,2), R(0,2) - R(2,0), R(1,0) - R(0,1));
return (std::abs(theta) < 0.001) ? (0.5 * K) : (0.5 * theta / std::sin(theta) * K);
}
template<typename T>
Eigen::Matrix<T, 3, 1> RotMtoEuler(const Eigen::Matrix<T, 3, 3> &rot)
{
T sy = sqrt(rot(0,0)*rot(0,0) + rot(1,0)*rot(1,0));
bool singular = sy < 1e-6;
T x, y, z;
if(!singular)
{
x = atan2(rot(2, 1), rot(2, 2));
y = atan2(-rot(2, 0), sy);
z = atan2(rot(1, 0), rot(0, 0));
}
else
{
x = atan2(-rot(1, 2), rot(1, 1));
y = atan2(-rot(2, 0), sy);
z = 0;
}
Eigen::Matrix<T, 3, 1> ang(x, y, z);
return ang;
}
#endif
point_lio_ros2/launch/correct_odom_unilidar_l1.launch.py
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from launch import LaunchDescription
from launch.actions import GroupAction, DeclareLaunchArgument
from launch.conditions import IfCondition
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
def generate_launch_description():
# Node parameters, including those from the YAML configuration file
laser_mapping_params = [
PathJoinSubstitution([
FindPackageShare('point_lio'),
'config', 'unilidar_l1.yaml'
]),
{
'use_imu_as_input': False, # Change to True to use IMU as input of Point-LIO
'prop_at_freq_of_imu': True,
'check_satu': True,
'init_map_size': 10,
'point_filter_num': 1, # Options: 1, 3
'space_down_sample': True,
'filter_size_surf': 0.1, # Options: 0.5, 0.3, 0.2, 0.15, 0.1
'filter_size_map': 0.1, # Options: 0.5, 0.3, 0.15, 0.1
'cube_side_length': 1000.0, # Option: 1000
'runtime_pos_log_enable': False, # Option: True
'odom_only': True, # Option: False
'odom_header_frame_id': "odom", # Default: "camera_init"
'odom_child_frame_id': "base_link", # Default: "aft_mapped"
}
]
# Node definition for laserMapping with Point-LIO
laser_mapping_node = Node(
package='point_lio',
executable='pointlio_mapping',
name='laserMapping',
output='screen',
parameters=laser_mapping_params,
# prefix='gdb -ex run --args'
)
# Assemble the launch description
ld = LaunchDescription([
laser_mapping_node,
])
return ld
point_lio_ros2/launch/correct_odom_unilidar_l2.launch.py
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from launch import LaunchDescription
from launch.actions import GroupAction, DeclareLaunchArgument
from launch.conditions import IfCondition
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
def generate_launch_description():
# Node parameters, including those from the YAML configuration file
laser_mapping_params = [
PathJoinSubstitution([
FindPackageShare('point_lio'),
'config', 'unilidar_l2.yaml'
]),
{
'use_imu_as_input': False, # Change to True to use IMU as input of Point-LIO
'prop_at_freq_of_imu': True,
'check_satu': True,
'init_map_size': 10,
'point_filter_num': 1, # Options: 1, 3
'space_down_sample': True,
'filter_size_surf': 0.1, # Options: 0.5, 0.3, 0.2, 0.15, 0.1
'filter_size_map': 0.1, # Options: 0.5, 0.3, 0.15, 0.1
'cube_side_length': 1000.0, # Option: 1000
'runtime_pos_log_enable': False, # Option: True
'odom_only': True, # Option: False
'odom_header_frame_id': "odom", # Default: "camera_init"
'odom_child_frame_id': "base_link", # Default: "aft_mapped"
}
]
# Node definition for laserMapping with Point-LIO
laser_mapping_node = Node(
package='point_lio',
executable='pointlio_mapping',
name='laserMapping',
output='screen',
parameters=laser_mapping_params,
# prefix='gdb -ex run --args'
)
# Assemble the launch description
ld = LaunchDescription([
laser_mapping_node,
])
return ld
point_lio_ros2/launch/gdb_debug_example.launch.py
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from launch import LaunchDescription
from launch.actions import GroupAction, DeclareLaunchArgument
from launch.conditions import IfCondition
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
def generate_launch_description():
# Declare the RViz argument
rviz_arg = DeclareLaunchArgument(
'rviz', default_value='true',
description='Flag to launch RViz.')
# Node parameters, including those from the YAML configuration file
laser_mapping_params = [
{
'use_imu_as_input': False, # Change to True to use IMU as input of Point-LIO
'prop_at_freq_of_imu': True,
'check_satu': True,
'init_map_size': 10,
'point_filter_num': 1, # Options: 1, 3
'space_down_sample': True,
'filter_size_surf': 0.3, # Options: 0.5, 0.3, 0.2, 0.15, 0.1
'filter_size_map': 0.2, # Options: 0.5, 0.3, 0.15, 0.1
'cube_side_length': 1000.0, # Option: 1000, 2000
'runtime_pos_log_enable': False # Option: True
},
# PathJoinSubstitution([
# FindPackageShare('point_lio'),
# 'config', 'horizon.yaml'
# ])
]
# Node definition for laserMapping with Point-LIO
laser_mapping_node = Node(
package='point_lio',
executable='pointlio_mapping',
name='laserMapping',
output='screen',
parameters=laser_mapping_params,
prefix='gdb -ex run --args'
)
# Conditional RViz node launch
rviz_node = Node(
package='rviz2',
executable='rviz2',
name='rviz',
arguments=['-d', PathJoinSubstitution([
FindPackageShare('point_lio'),
'rviz_cfg', 'loam_livox.rviz'
])],
condition=IfCondition(LaunchConfiguration('rviz')),
prefix='nice'
)
# Assemble the launch description
ld = LaunchDescription([
rviz_arg,
laser_mapping_node,
GroupAction(
actions=[rviz_node],
condition=IfCondition(LaunchConfiguration('rviz'))
),
])
return ld
point_lio_ros2/launch/mapping_avia.launch.py
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from launch import LaunchDescription
from launch.actions import GroupAction, DeclareLaunchArgument
from launch.conditions import IfCondition
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
def generate_launch_description():
# Declare the RViz argument
rviz_arg = DeclareLaunchArgument(
'rviz', default_value='true',
description='Flag to launch RViz.')
# Node parameters, including those from the YAML configuration file
laser_mapping_params = [
PathJoinSubstitution([
FindPackageShare('point_lio'),
'config', 'avia.yaml'
]),
{
'use_imu_as_input': False, # Change to True to use IMU as input of Point-LIO
'prop_at_freq_of_imu': True,
'check_satu': True,
'init_map_size': 10,
'point_filter_num': 1, # options: 4, 3
'space_down_sample': True,
'filter_size_surf': 0.3, # options: 0.5, 0.3, 0.2, 0.15, 0.1
'filter_size_map': 0.2, # options: 0.5, 0.3, 0.15, 0.1
'cube_side_length': 2000.0, # option: 1000
'runtime_pos_log_enable': False, # option: True
},
]
# Node definition for laserMapping with Point-LIO
laser_mapping_node = Node(
package='point_lio',
executable='pointlio_mapping',
name='laserMapping',
output='screen',
parameters=laser_mapping_params,
# prefix='gdb -ex run --args'
)
# Conditional RViz node launch
rviz_node = Node(
package='rviz2',
executable='rviz2',
name='rviz',
arguments=['-d', PathJoinSubstitution([
FindPackageShare('point_lio'),
'rviz_cfg', 'loam_livox.rviz'
])],
condition=IfCondition(LaunchConfiguration('rviz')),
prefix='nice'
)
# Assemble the launch description
ld = LaunchDescription([
rviz_arg,
laser_mapping_node,
GroupAction(
actions=[rviz_node],
condition=IfCondition(LaunchConfiguration('rviz'))
),
])
return ld
point_lio_ros2/launch/mapping_horizon.launch.py
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from launch import LaunchDescription
from launch.actions import GroupAction, DeclareLaunchArgument
from launch.conditions import IfCondition
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
def generate_launch_description():
# Declare the RViz argument
rviz_arg = DeclareLaunchArgument(
'rviz', default_value='true',
description='Flag to launch RViz.')
# Node parameters, including those from the YAML configuration file
laser_mapping_params = [
PathJoinSubstitution([
FindPackageShare('point_lio'),
'config', 'horizon.yaml'
]),
{
'use_imu_as_input': False, # Change to True to use IMU as input of Point-LIO
'prop_at_freq_of_imu': True,
'check_satu': True,
'init_map_size': 10,
'point_filter_num': 3, # Options: 1, 3
'space_down_sample': True,
'filter_size_surf': 0.5, # Options: 0.5, 0.3, 0.2, 0.15, 0.1
'filter_size_map': 0.5, # Options: 0.5, 0.3, 0.15, 0.1
'cube_side_length': 1000.0, # Option: 1000
'runtime_pos_log_enable': False, # Option: True
}
]
# Node definition for laserMapping with Point-LIO
laser_mapping_node = Node(
package='point_lio',
executable='pointlio_mapping',
name='laserMapping',
output='screen',
parameters=laser_mapping_params,
# prefix='gdb -ex run --args'
)
# Conditional RViz node launch
rviz_node = Node(
package='rviz2',
executable='rviz2',
name='rviz',
arguments=['-d', PathJoinSubstitution([
FindPackageShare('point_lio'),
'rviz_cfg', 'loam_livox.rviz'
])],
condition=IfCondition(LaunchConfiguration('rviz')),
prefix='nice'
)
# Assemble the launch description
ld = LaunchDescription([
rviz_arg,
laser_mapping_node,
GroupAction(
actions=[rviz_node],
condition=IfCondition(LaunchConfiguration('rviz'))
),
])
return ld
point_lio_ros2/launch/mapping_mid360.launch.py
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from launch import LaunchDescription
from launch.actions import GroupAction, DeclareLaunchArgument
from launch.conditions import IfCondition
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
def generate_launch_description():
# Declare the RViz argument
rviz_arg = DeclareLaunchArgument(
'rviz', default_value='true',
description='Flag to launch RViz.')
# Node parameters, including those from the YAML configuration file
laser_mapping_params = [
PathJoinSubstitution([
FindPackageShare('point_lio'),
'config', 'mid360.yaml'
]),
{
'use_imu_as_input': False, # Change to True to use IMU as input of Point-LIO
'prop_at_freq_of_imu': True,
'check_satu': True,
'init_map_size': 10,
'point_filter_num': 3, # Options: 1, 3
'space_down_sample': True,
'filter_size_surf': 0.5, # Options: 0.5, 0.3, 0.2, 0.15, 0.1
'filter_size_map': 0.5, # Options: 0.5, 0.3, 0.15, 0.1
'cube_side_length': 1000.0, # Option: 1000
'runtime_pos_log_enable': False, # Option: True
}
]
# Node definition for laserMapping with Point-LIO
laser_mapping_node = Node(
package='point_lio',
executable='pointlio_mapping',
name='laserMapping',
output='screen',
parameters=laser_mapping_params,
# prefix='gdb -ex run --args'
)
# Conditional RViz node launch
rviz_node = Node(
package='rviz2',
executable='rviz2',
name='rviz',
arguments=['-d', PathJoinSubstitution([
FindPackageShare('point_lio'),
'rviz_cfg', 'loam_livox.rviz'
])],
condition=IfCondition(LaunchConfiguration('rviz')),
prefix='nice'
)
# Assemble the launch description
ld = LaunchDescription([
rviz_arg,
laser_mapping_node,
GroupAction(
actions=[rviz_node],
condition=IfCondition(LaunchConfiguration('rviz'))
),
])
return ld
point_lio_ros2/launch/mapping_ouster64.launch.py
-68
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@@ -1,68 +0,0 @@
from launch import LaunchDescription
from launch.actions import GroupAction, DeclareLaunchArgument
from launch.conditions import IfCondition
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
def generate_launch_description():
# Declare the RViz argument
rviz_arg = DeclareLaunchArgument(
'rviz', default_value='true',
description='Flag to launch RViz.')
# Node parameters, including those from the YAML configuration file
laser_mapping_params = [
PathJoinSubstitution([
FindPackageShare('point_lio'),
'config', 'ouster64.yaml'
]),
{
'use_imu_as_input': False, # Change to True to use IMU as input of Point-LIO
'prop_at_freq_of_imu': True,
'check_satu': True,
'init_map_size': 10,
'point_filter_num': 4, # Options: 4, 3
'space_down_sample': True,
'filter_size_surf': 0.5, # Options: 0.5, 0.3, 0.2, 0.15, 0.1
'filter_size_map': 0.5, # Options: 0.5, 0.3, 0.15, 0.1
'cube_side_length': 1000.0, # Option: 1000 (changed from 2000)
'runtime_pos_log_enable': False, # Option: True
}
]
# Node definition for laserMapping with Point-LIO
laser_mapping_node = Node(
package='point_lio',
executable='pointlio_mapping',
name='laserMapping',
output='screen',
parameters=laser_mapping_params,
# prefix='gdb -ex run --args'
)
# Conditional RViz node launch
rviz_node = Node(
package='rviz2',
executable='rviz2',
name='rviz',
arguments=['-d', PathJoinSubstitution([
FindPackageShare('point_lio'),
'rviz_cfg', 'loam_livox.rviz'
])],
condition=IfCondition(LaunchConfiguration('rviz')),
prefix='nice'
)
# Assemble the launch description
ld = LaunchDescription([
rviz_arg,
laser_mapping_node,
GroupAction(
actions=[rviz_node],
condition=IfCondition(LaunchConfiguration('rviz'))
),
])
return ld
point_lio_ros2/launch/mapping_unilidar_l1.launch.py
-68
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@@ -1,68 +0,0 @@
from launch import LaunchDescription
from launch.actions import GroupAction, DeclareLaunchArgument
from launch.conditions import IfCondition
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
def generate_launch_description():
# Declare the RViz argument
rviz_arg = DeclareLaunchArgument(
'rviz', default_value='true',
description='Flag to launch RViz.')
# Node parameters, including those from the YAML configuration file
laser_mapping_params = [
PathJoinSubstitution([
FindPackageShare('point_lio'),
'config', 'unilidar_l1.yaml'
]),
{
'use_imu_as_input': False, # Change to True to use IMU as input of Point-LIO
'prop_at_freq_of_imu': True,
'check_satu': True,
'init_map_size': 10,
'point_filter_num': 1, # Options: 1, 3
'space_down_sample': True,
'filter_size_surf': 0.1, # Options: 0.5, 0.3, 0.2, 0.15, 0.1
'filter_size_map': 0.1, # Options: 0.5, 0.3, 0.15, 0.1
'cube_side_length': 1000.0, # Option: 1000
'runtime_pos_log_enable': False, # Option: True
}
]
# Node definition for laserMapping with Point-LIO
laser_mapping_node = Node(
package='point_lio',
executable='pointlio_mapping',
name='laserMapping',
output='screen',
parameters=laser_mapping_params,
# prefix='gdb -ex run --args'
)
# Conditional RViz node launch
rviz_node = Node(
package='rviz2',
executable='rviz2',
name='rviz',
arguments=['-d', PathJoinSubstitution([
FindPackageShare('point_lio'),
'rviz_cfg', 'loam_livox.rviz'
])],
condition=IfCondition(LaunchConfiguration('rviz')),
prefix='nice'
)
# Assemble the launch description
ld = LaunchDescription([
rviz_arg,
laser_mapping_node,
GroupAction(
actions=[rviz_node],
condition=IfCondition(LaunchConfiguration('rviz'))
),
])
return ld
point_lio_ros2/launch/mapping_unilidar_l2.launch.py
-70
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@@ -1,70 +0,0 @@
from launch import LaunchDescription
from launch.actions import GroupAction, DeclareLaunchArgument
from launch.conditions import IfCondition
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
def generate_launch_description():
# Declare the RViz argument
rviz_arg = DeclareLaunchArgument(
'rviz', default_value='true',
description='Flag to launch RViz.')
# Node parameters, including those from the YAML configuration file
laser_mapping_params = [
PathJoinSubstitution([
FindPackageShare('point_lio'),
'config', 'unilidar_l2.yaml'
]),
{
'use_imu_as_input': True, # Change to True to use IMU as input of Point-LIO
'prop_at_freq_of_imu': True,
'check_satu': True,
'init_map_size': 10,
'point_filter_num': 3, # Options: 1, 3
'space_down_sample': True,
'filter_size_surf': 0.1, # Options: 0.5, 0.3, 0.2, 0.15, 0.1
'filter_size_map': 0.1, # Options: 0.5, 0.3, 0.15, 0.1
'cube_side_length': 500.0, # Option: 1000
'runtime_pos_log_enable': False, # Option: True
}
]
# Node definition for laserMapping with Point-LIO
laser_mapping_node = Node(
package='point_lio',
executable='pointlio_mapping',
name='laserMapping',
output='screen',
parameters=laser_mapping_params,
# prefix='gdb -ex run --args'
)
# Conditional RViz node launch
rviz_node = Node(
package='rviz2',
executable='rviz2',
name='rviz',
arguments=['-d', PathJoinSubstitution([
FindPackageShare('point_lio'),
'rviz_cfg', 'loam_livox.rviz'
])],
condition=IfCondition(LaunchConfiguration('rviz')),
prefix='nice'
)
# Assemble the launch description
ld = LaunchDescription([
rviz_arg,
laser_mapping_node,
GroupAction(
actions=[rviz_node],
condition=IfCondition(LaunchConfiguration('rviz'))
),
])
return ld
point_lio_ros2/launch/mapping_velody16.launch.py
-68
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@@ -1,68 +0,0 @@
from launch import LaunchDescription
from launch.actions import GroupAction, DeclareLaunchArgument
from launch.conditions import IfCondition
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
def generate_launch_description():
# Declare the RViz argument
rviz_arg = DeclareLaunchArgument(
'rviz', default_value='true',
description='Flag to launch RViz.')
# Node parameters, including those from the YAML configuration file
laser_mapping_params = [
PathJoinSubstitution([
FindPackageShare('point_lio'),
'config', 'velody16.yaml'
]),
{
'use_imu_as_input': False, # Change to True to use IMU as input of Point-LIO
'prop_at_freq_of_imu': True,
'check_satu': True,
'init_map_size': 10,
'point_filter_num': 4, # Options: 4, 3
'space_down_sample': True,
'filter_size_surf': 0.5, # Options: 0.5, 0.3, 0.2, 0.15, 0.1
'filter_size_map': 0.5, # Options: 0.5, 0.3, 0.15, 0.1
'cube_side_length': 1000.0, # Option: 1000 (changed from 2000)
'runtime_pos_log_enable': False, # Option: True
}
]
# Node definition for laserMapping with Point-LIO
laser_mapping_node = Node(
package='point_lio',
executable='pointlio_mapping',
name='laserMapping',
output='screen',
parameters=laser_mapping_params,
# prefix='gdb -ex run --args'
)
# Conditional RViz node launch
rviz_node = Node(
package='rviz2',
executable='rviz2',
name='rviz',
arguments=['-d', PathJoinSubstitution([
FindPackageShare('point_lio'),
'rviz_cfg', 'loam_livox.rviz'
])],
condition=IfCondition(LaunchConfiguration('rviz')),
prefix='nice'
)
# Assemble the launch description
ld = LaunchDescription([
rviz_arg,
laser_mapping_node,
GroupAction(
actions=[rviz_node],
condition=IfCondition(LaunchConfiguration('rviz'))
),
])
return ld
point_lio_ros2/package.xml
-40
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@@ -1,40 +0,0 @@
<?xml version="1.0"?>
<package format="3">
<name>point_lio</name>
<version>0.0.0</version>
<description>
This is a modified version of LOAM which is original algorithm
is described in the following paper:
J. Zhang and S. Singh. LOAM: Lidar Odometry and Mapping in Real-time.
Robotics: Science and Systems Conference (RSS). Berkeley, CA, July 2014.
</description>
<maintainer email="dflorea@uc.cl">Daniel Florea</maintainer>
<license>BSD</license>
<author email="dflorea@uc.cl">Daniel Florea</author>
<!-- ROS2 uses ament as build system -->
<buildtool_depend>ament_cmake</buildtool_depend>
<!-- Dependencies are now also categorized as build, build_export, and exec_depend -->
<depend>rclcpp</depend> <!-- roscpp in ROS1 is replaced by rclcpp in ROS2 -->
<depend>rclpy</depend> <!-- rospy is replaced by rclpy in ROS2 -->
<depend>sensor_msgs</depend>
<depend>geometry_msgs</depend>
<depend>nav_msgs</depend>
<depend>tf2_ros</depend> <!-- tf in ROS1 is replaced by tf2 in ROS2 -->
<depend>pcl_ros</depend> <!-- For PCL support -->
<depend>pcl_conversions</depend> <!-- For PCL support -->
<depend>visualization_msgs</depend> <!-- For visualization_msgs support -->
<!-- <depend>livox_ros_driver2</depend> -->
<!-- test_depend remains the same, but make sure the testing tools you use are compatible with ROS2 -->
<test_depend>ament_lint_auto</test_depend>
<test_depend>ament_lint_common</test_depend>
<export>
<build_type>ament_cmake</build_type>
</export>
</package>
point_lio_ros2/rviz_cfg/loam_livox.rviz
-326
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@@ -1,326 +0,0 @@
Panels:
- Class: rviz_common/Displays
Help Height: 78
Name: Displays
Property Tree Widget:
Expanded:
- /Global Options1
- /Status1
Splitter Ratio: 0.5
Tree Height: 549
- Class: rviz_common/Selection
Name: Selection
- Class: rviz_common/Tool Properties
Expanded:
- /2D Goal Pose1
- /Publish Point1
Name: Tool Properties
Splitter Ratio: 0.5886790156364441
- Class: rviz_common/Views
Expanded:
- /Current View1
Name: Views
Splitter Ratio: 0.5
- Class: rviz_common/Time
Experimental: false
Name: Time
SyncMode: 0
SyncSource: CloudRegistered
Visualization Manager:
Class: ""
Displays:
- Class: rviz_default_plugins/TF
Enabled: true
Frame Timeout: 15
Frames:
All Enabled: true
aft_mapped:
Value: true
base_footprint:
Value: true
base_link:
Value: true
camera_init:
Value: true
camera_link:
Value: true
gyro_link:
Value: true
laser:
Value: true
left_front_link:
Value: true
left_wheel_link:
Value: true
map:
Value: true
odom_combined:
Value: true
right_front_link:
Value: true
right_wheel_link:
Value: true
Marker Scale: 1
Name: TF
Show Arrows: true
Show Axes: true
Show Names: false
Tree:
camera_init:
aft_mapped:
{}
Update Interval: 0
Value: true
- Angle Tolerance: 0.10000000149011612
Class: rviz_default_plugins/Odometry
Covariance:
Orientation:
Alpha: 0.5
Color: 255; 255; 127
Color Style: Unique
Frame: Local
Offset: 1
Scale: 1
Value: true
Position:
Alpha: 0.30000001192092896
Color: 204; 51; 204
Scale: 1
Value: true
Value: true
Enabled: true
Keep: 100
Name: Odometry
Position Tolerance: 0.10000000149011612
Shape:
Alpha: 1
Axes Length: 1
Axes Radius: 0.10000000149011612
Color: 255; 25; 0
Head Length: 0.30000001192092896
Head Radius: 0.10000000149011612
Shaft Length: 1
Shaft Radius: 0.05000000074505806
Value: Arrow
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /Odometry
Value: true
- Alpha: 1
Buffer Length: 1
Class: rviz_default_plugins/Path
Color: 25; 255; 0
Enabled: true
Head Diameter: 0.30000001192092896
Head Length: 0.20000000298023224
Length: 0.30000001192092896
Line Style: Lines
Line Width: 0.029999999329447746
Name: Path
Offset:
X: 0
Y: 0
Z: 0
Pose Color: 255; 85; 255
Pose Style: None
Radius: 0.029999999329447746
Shaft Diameter: 0.10000000149011612
Shaft Length: 0.10000000149011612
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /path
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 1.8584054708480835
Min Value: -0.10289539396762848
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: AxisColor
Decay Time: 30
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 186
Min Color: 0; 0; 0
Min Intensity: 0
Name: CloudRegistered
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Flat Squares
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /cloud_registered
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 184
Min Color: 0; 0; 0
Min Intensity: 0
Name: CloudEffected
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.019999999552965164
Style: Flat Squares
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /cloud_effected
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 2.036320447921753
Min Value: -0.09378375858068466
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: AxisColor
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 255
Min Color: 0; 0; 0
Min Intensity: 0
Name: CloudMap
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.019999999552965164
Style: Flat Squares
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /Laser_map
Use Fixed Frame: true
Use rainbow: true
Value: true
Enabled: true
Global Options:
Background Color: 0; 0; 0
Fixed Frame: camera_init
Frame Rate: 30
Name: root
Tools:
- Class: rviz_default_plugins/Interact
Hide Inactive Objects: true
- Class: rviz_default_plugins/MoveCamera
- Class: rviz_default_plugins/Select
- Class: rviz_default_plugins/FocusCamera
- Class: rviz_default_plugins/Measure
Line color: 128; 128; 0
- Class: rviz_default_plugins/SetInitialPose
Covariance x: 0.25
Covariance y: 0.25
Covariance yaw: 0.06853891909122467
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /initialpose
- Class: rviz_default_plugins/SetGoal
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /goal_pose
- Class: rviz_default_plugins/PublishPoint
Single click: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /clicked_point
Transformation:
Current:
Class: rviz_default_plugins/TF
Value: true
Views:
Current:
Class: rviz_default_plugins/Orbit
Distance: 28.403661727905273
Enable Stereo Rendering:
Stereo Eye Separation: 0.05999999865889549
Stereo Focal Distance: 1
Swap Stereo Eyes: false
Value: false
Focal Point:
X: -2.2604103088378906
Y: -0.3224470913410187
Z: 0.9725424647331238
Focal Shape Fixed Size: true
Focal Shape Size: 0.05000000074505806
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
Pitch: 0.7497965693473816
Target Frame: <Fixed Frame>
Value: Orbit (rviz_default_plugins)
Yaw: 1.7503817081451416
Saved: ~
Window Geometry:
Displays:
collapsed: false
Height: 846
Hide Left Dock: false
Hide Right Dock: false
QMainWindow State: 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
Selection:
collapsed: false
Time:
collapsed: false
Tool Properties:
collapsed: false
Views:
collapsed: false
Width: 1453
X: 2404
Y: 218
point_lio_ros2/src/Estimator.cpp
-435
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@@ -1,435 +0,0 @@
// #include <../include/IKFoM/IKFoM_toolkit/esekfom/esekfom.hpp>
#include "Estimator.h"
PointCloudXYZI::Ptr normvec(new PointCloudXYZI(100000, 1));
std::vector<int> time_seq;
PointCloudXYZI::Ptr feats_down_body(new PointCloudXYZI());
PointCloudXYZI::Ptr feats_down_world(new PointCloudXYZI());
std::vector<V3D> pbody_list;
std::vector<PointVector> Nearest_Points;
KD_TREE<PointType> ikdtree;
std::vector<float> pointSearchSqDis(NUM_MATCH_POINTS);
bool point_selected_surf[100000] = {0};
std::vector<M3D> crossmat_list;
int effct_feat_num = 0;
int k;
int idx;
esekfom::esekf<state_input, 24, input_ikfom> kf_input;
esekfom::esekf<state_output, 30, input_ikfom> kf_output;
state_input state_in;
state_output state_out;
input_ikfom input_in;
V3D angvel_avr, acc_avr;
V3D Lidar_T_wrt_IMU(Zero3d);
M3D Lidar_R_wrt_IMU(Eye3d);
typedef MTK::vect<3, double> vect3;
typedef MTK::SO3<double> SO3;
typedef MTK::S2<double, 98090, 10000, 1> S2;
typedef MTK::vect<1, double> vect1;
typedef MTK::vect<2, double> vect2;
Eigen::Matrix<double, 24, 24> process_noise_cov_input()
{
Eigen::Matrix<double, 24, 24> cov;
cov.setZero();
cov.block<3, 3>(3, 3).diagonal() << gyr_cov_input, gyr_cov_input, gyr_cov_input;
cov.block<3, 3>(12, 12).diagonal() << acc_cov_input, acc_cov_input, acc_cov_input;
cov.block<3, 3>(15, 15).diagonal() << b_gyr_cov, b_gyr_cov, b_gyr_cov;
cov.block<3, 3>(18, 18).diagonal() << b_acc_cov, b_acc_cov, b_acc_cov;
// MTK::get_cov<process_noise_input>::type cov = MTK::get_cov<process_noise_input>::type::Zero();
// MTK::setDiagonal<process_noise_input, vect3, 0>(cov, &process_noise_input::ng, gyr_cov_input);// 0.03
// MTK::setDiagonal<process_noise_input, vect3, 3>(cov, &process_noise_input::na, acc_cov_input); // *dt 0.01 0.01 * dt * dt 0.05
// MTK::setDiagonal<process_noise_input, vect3, 6>(cov, &process_noise_input::nbg, b_gyr_cov); // *dt 0.00001 0.00001 * dt *dt 0.3 //0.001 0.0001 0.01
// MTK::setDiagonal<process_noise_input, vect3, 9>(cov, &process_noise_input::nba, b_acc_cov); //0.001 0.05 0.0001/out 0.01
return cov;
}
Eigen::Matrix<double, 30, 30> process_noise_cov_output()
{
Eigen::Matrix<double, 30, 30> cov;
cov.setZero();
cov.block<3, 3>(12, 12).diagonal() << vel_cov, vel_cov, vel_cov;
cov.block<3, 3>(15, 15).diagonal() << gyr_cov_output, gyr_cov_output, gyr_cov_output;
cov.block<3, 3>(18, 18).diagonal() << acc_cov_output, acc_cov_output, acc_cov_output;
cov.block<3, 3>(24, 24).diagonal() << b_gyr_cov, b_gyr_cov, b_gyr_cov;
cov.block<3, 3>(27, 27).diagonal() << b_acc_cov, b_acc_cov, b_acc_cov;
// MTK::get_cov<process_noise_output>::type cov = MTK::get_cov<process_noise_output>::type::Zero();
// MTK::setDiagonal<process_noise_output, vect3, 0>(cov, &process_noise_output::vel, vel_cov);// 0.03
// MTK::setDiagonal<process_noise_output, vect3, 3>(cov, &process_noise_output::ng, gyr_cov_output); // *dt 0.01 0.01 * dt * dt 0.05
// MTK::setDiagonal<process_noise_output, vect3, 6>(cov, &process_noise_output::na, acc_cov_output); // *dt 0.00001 0.00001 * dt *dt 0.3 //0.001 0.0001 0.01
// MTK::setDiagonal<process_noise_output, vect3, 9>(cov, &process_noise_output::nbg, b_gyr_cov); //0.001 0.05 0.0001/out 0.01
// MTK::setDiagonal<process_noise_output, vect3, 12>(cov, &process_noise_output::nba, b_acc_cov); //0.001 0.05 0.0001/out 0.01
return cov;
}
Eigen::Matrix<double, 24, 1> get_f_input(state_input &s, const input_ikfom &in)
{
Eigen::Matrix<double, 24, 1> res = Eigen::Matrix<double, 24, 1>::Zero();
vect3 omega;
in.gyro.boxminus(omega, s.bg);
vect3 a_inertial = s.rot.normalized() * (in.acc-s.ba);
for(int i = 0; i < 3; i++ ){
res(i) = s.vel[i];
res(i + 3) = omega[i];
res(i + 12) = a_inertial[i] + s.gravity[i];
}
return res;
}
Eigen::Matrix<double, 30, 1> get_f_output(state_output &s, const input_ikfom &in)
{
Eigen::Matrix<double, 30, 1> res = Eigen::Matrix<double, 30, 1>::Zero();
vect3 a_inertial = s.rot.normalized() * s.acc;
for(int i = 0; i < 3; i++ ){
res(i) = s.vel[i];
res(i + 3) = s.omg[i];
res(i + 12) = a_inertial[i] + s.gravity[i];
}
return res;
}
Eigen::Matrix<double, 24, 24> df_dx_input(state_input &s, const input_ikfom &in)
{
Eigen::Matrix<double, 24, 24> cov = Eigen::Matrix<double, 24, 24>::Zero();
cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity();
vect3 acc_;
in.acc.boxminus(acc_, s.ba);
vect3 omega;
in.gyro.boxminus(omega, s.bg);
cov.template block<3, 3>(12, 3) = -s.rot.normalized().toRotationMatrix()*MTK::hat(acc_);
cov.template block<3, 3>(12, 18) = -s.rot.normalized().toRotationMatrix();
// Eigen::Matrix<state_ikfom::scalar, 2, 1> vec = Eigen::Matrix<state_ikfom::scalar, 2, 1>::Zero();
// Eigen::Matrix<state_ikfom::scalar, 3, 2> grav_matrix;
// s.S2_Mx(grav_matrix, vec, 21);
cov.template block<3, 3>(12, 21) = Eigen::Matrix3d::Identity(); // grav_matrix;
cov.template block<3, 3>(3, 15) = -Eigen::Matrix3d::Identity();
return cov;
}
// Eigen::Matrix<double, 24, 12> df_dw_input(state_input &s, const input_ikfom &in)
// {
// Eigen::Matrix<double, 24, 12> cov = Eigen::Matrix<double, 24, 12>::Zero();
// cov.template block<3, 3>(12, 3) = -s.rot.normalized().toRotationMatrix();
// cov.template block<3, 3>(3, 0) = -Eigen::Matrix3d::Identity();
// cov.template block<3, 3>(15, 6) = Eigen::Matrix3d::Identity();
// cov.template block<3, 3>(18, 9) = Eigen::Matrix3d::Identity();
// return cov;
// }
Eigen::Matrix<double, 30, 30> df_dx_output(state_output &s, const input_ikfom &in)
{
Eigen::Matrix<double, 30, 30> cov = Eigen::Matrix<double, 30, 30>::Zero();
cov.template block<3, 3>(0, 12) = Eigen::Matrix3d::Identity();
cov.template block<3, 3>(12, 3) = -s.rot.normalized().toRotationMatrix()*MTK::hat(s.acc);
cov.template block<3, 3>(12, 18) = s.rot.normalized().toRotationMatrix();
// Eigen::Matrix<state_ikfom::scalar, 2, 1> vec = Eigen::Matrix<state_ikfom::scalar, 2, 1>::Zero();
// Eigen::Matrix<state_ikfom::scalar, 3, 2> grav_matrix;
// s.S2_Mx(grav_matrix, vec, 21);
cov.template block<3, 3>(12, 21) = Eigen::Matrix3d::Identity(); // grav_matrix;
cov.template block<3, 3>(3, 15) = Eigen::Matrix3d::Identity();
return cov;
}
// Eigen::Matrix<double, 30, 15> df_dw_output(state_output &s)
// {
// Eigen::Matrix<double, 30, 15> cov = Eigen::Matrix<double, 30, 15>::Zero();
// cov.template block<3, 3>(12, 0) = Eigen::Matrix3d::Identity();
// cov.template block<3, 3>(15, 3) = Eigen::Matrix3d::Identity();
// cov.template block<3, 3>(18, 6) = Eigen::Matrix3d::Identity();
// cov.template block<3, 3>(24, 9) = Eigen::Matrix3d::Identity();
// cov.template block<3, 3>(27, 12) = Eigen::Matrix3d::Identity();
// return cov;
// }
vect3 SO3ToEuler(const SO3 &orient)
{
Eigen::Matrix<double, 3, 1> _ang;
Eigen::Vector4d q_data = orient.coeffs().transpose();
//scalar w=orient.coeffs[3], x=orient.coeffs[0], y=orient.coeffs[1], z=orient.coeffs[2];
double sqw = q_data[3]*q_data[3];
double sqx = q_data[0]*q_data[0];
double sqy = q_data[1]*q_data[1];
double sqz = q_data[2]*q_data[2];
double unit = sqx + sqy + sqz + sqw; // if normalized is one, otherwise is correction factor
double test = q_data[3]*q_data[1] - q_data[2]*q_data[0];
if (test > 0.49999*unit) { // singularity at north pole
_ang << 2 * std::atan2(q_data[0], q_data[3]), M_PI/2, 0;
double temp[3] = {_ang[0] * 57.3, _ang[1] * 57.3, _ang[2] * 57.3};
vect3 euler_ang(temp, 3);
return euler_ang;
}
if (test < -0.49999*unit) { // singularity at south pole
_ang << -2 * std::atan2(q_data[0], q_data[3]), -M_PI/2, 0;
double temp[3] = {_ang[0] * 57.3, _ang[1] * 57.3, _ang[2] * 57.3};
vect3 euler_ang(temp, 3);
return euler_ang;
}
_ang <<
std::atan2(2*q_data[0]*q_data[3]+2*q_data[1]*q_data[2] , -sqx - sqy + sqz + sqw),
std::asin (2*test/unit),
std::atan2(2*q_data[2]*q_data[3]+2*q_data[1]*q_data[0] , sqx - sqy - sqz + sqw);
double temp[3] = {_ang[0] * 57.3, _ang[1] * 57.3, _ang[2] * 57.3};
vect3 euler_ang(temp, 3);
return euler_ang;
}
void h_model_input(state_input &s, esekfom::dyn_share_modified<double> &ekfom_data)
{
bool match_in_map = false;
VF(4) pabcd;
pabcd.setZero();
normvec->resize(time_seq[k]);
int effect_num_k = 0;
for (int j = 0; j < time_seq[k]; j++)
{
PointType &point_body_j = feats_down_body->points[idx+j+1];
PointType &point_world_j = feats_down_world->points[idx+j+1];
pointBodyToWorld(&point_body_j, &point_world_j);
V3D p_body = pbody_list[idx+j+1];
V3D p_world;
p_world << point_world_j.x, point_world_j.y, point_world_j.z;
{
auto &points_near = Nearest_Points[idx+j+1];
ikdtree.Nearest_Search(point_world_j, NUM_MATCH_POINTS, points_near, pointSearchSqDis, 2.236); //1.0); //, 3.0); // 2.236;
if ((points_near.size() < NUM_MATCH_POINTS) || pointSearchSqDis[NUM_MATCH_POINTS - 1] > 5) // 5)
{
point_selected_surf[idx+j+1] = false;
}
else
{
point_selected_surf[idx+j+1] = false;
if (esti_plane(pabcd, points_near, plane_thr)) //(planeValid)
{
float pd2 = pabcd(0) * point_world_j.x + pabcd(1) * point_world_j.y + pabcd(2) * point_world_j.z + pabcd(3);
if (p_body.norm() > match_s * pd2 * pd2)
{
point_selected_surf[idx+j+1] = true;
normvec->points[j].x = pabcd(0);
normvec->points[j].y = pabcd(1);
normvec->points[j].z = pabcd(2);
normvec->points[j].intensity = pabcd(3);
effect_num_k ++;
}
}
}
}
}
if (effect_num_k == 0)
{
ekfom_data.valid = false;
return;
}
ekfom_data.M_Noise = laser_point_cov;
ekfom_data.h_x = Eigen::MatrixXd::Zero(effect_num_k, 12);
ekfom_data.z.resize(effect_num_k);
int m = 0;
for (int j = 0; j < time_seq[k]; j++)
{
if(point_selected_surf[idx+j+1])
{
V3D norm_vec(normvec->points[j].x, normvec->points[j].y, normvec->points[j].z);
if (extrinsic_est_en)
{
V3D p_body = pbody_list[idx+j+1];
M3D p_crossmat, p_imu_crossmat;
p_crossmat << SKEW_SYM_MATRX(p_body);
V3D point_imu = s.offset_R_L_I.normalized() * p_body + s.offset_T_L_I;
p_imu_crossmat << SKEW_SYM_MATRX(point_imu);
V3D C(s.rot.conjugate().normalized() * norm_vec);
V3D A(p_imu_crossmat * C);
V3D B(p_crossmat * s.offset_R_L_I.conjugate().normalized() * C);
ekfom_data.h_x.block<1, 12>(m, 0) << norm_vec(0), norm_vec(1), norm_vec(2), VEC_FROM_ARRAY(A), VEC_FROM_ARRAY(B), VEC_FROM_ARRAY(C);
}
else
{
M3D point_crossmat = crossmat_list[idx+j+1];
V3D C(s.rot.conjugate().normalized() * norm_vec);
V3D A(point_crossmat * C);
ekfom_data.h_x.block<1, 12>(m, 0) << norm_vec(0), norm_vec(1), norm_vec(2), VEC_FROM_ARRAY(A), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
ekfom_data.z(m) = -norm_vec(0) * feats_down_world->points[idx+j+1].x -norm_vec(1) * feats_down_world->points[idx+j+1].y -norm_vec(2) * feats_down_world->points[idx+j+1].z-normvec->points[j].intensity;
m++;
}
}
effct_feat_num += effect_num_k;
}
void h_model_output(state_output &s, esekfom::dyn_share_modified<double> &ekfom_data)
{
bool match_in_map = false;
VF(4) pabcd;
pabcd.setZero();
normvec->resize(time_seq[k]);
int effect_num_k = 0;
for (int j = 0; j < time_seq[k]; j++)
{
PointType &point_body_j = feats_down_body->points[idx+j+1];
PointType &point_world_j = feats_down_world->points[idx+j+1];
pointBodyToWorld(&point_body_j, &point_world_j);
V3D p_body = pbody_list[idx+j+1];
V3D p_world;
p_world << point_world_j.x, point_world_j.y, point_world_j.z;
{
auto &points_near = Nearest_Points[idx+j+1];
ikdtree.Nearest_Search(point_world_j, NUM_MATCH_POINTS, points_near, pointSearchSqDis, 2.236);
if ((points_near.size() < NUM_MATCH_POINTS) || pointSearchSqDis[NUM_MATCH_POINTS - 1] > 5)
{
point_selected_surf[idx+j+1] = false;
}
else
{
point_selected_surf[idx+j+1] = false;
if (esti_plane(pabcd, points_near, plane_thr)) //(planeValid)
{
float pd2 = pabcd(0) * point_world_j.x + pabcd(1) * point_world_j.y + pabcd(2) * point_world_j.z + pabcd(3);
if (p_body.norm() > match_s * pd2 * pd2)
{
// point_selected_surf[i] = true;
point_selected_surf[idx+j+1] = true;
normvec->points[j].x = pabcd(0);
normvec->points[j].y = pabcd(1);
normvec->points[j].z = pabcd(2);
normvec->points[j].intensity = pabcd(3);
effect_num_k ++;
}
}
}
}
}
if (effect_num_k == 0)
{
ekfom_data.valid = false;
return;
}
ekfom_data.M_Noise = laser_point_cov;
ekfom_data.h_x = Eigen::MatrixXd::Zero(effect_num_k, 12);
ekfom_data.z.resize(effect_num_k);
int m = 0;
for (int j = 0; j < time_seq[k]; j++)
{
if(point_selected_surf[idx+j+1])
{
V3D norm_vec(normvec->points[j].x, normvec->points[j].y, normvec->points[j].z);
if (extrinsic_est_en)
{
V3D p_body = pbody_list[idx+j+1];
M3D p_crossmat, p_imu_crossmat;
p_crossmat << SKEW_SYM_MATRX(p_body);
V3D point_imu = s.offset_R_L_I.normalized() * p_body + s.offset_T_L_I;
p_imu_crossmat << SKEW_SYM_MATRX(point_imu);
V3D C(s.rot.conjugate().normalized() * norm_vec);
V3D A(p_imu_crossmat * C);
V3D B(p_crossmat * s.offset_R_L_I.conjugate().normalized() * C);
ekfom_data.h_x.block<1, 12>(m, 0) << norm_vec(0), norm_vec(1), norm_vec(2), VEC_FROM_ARRAY(A), VEC_FROM_ARRAY(B), VEC_FROM_ARRAY(C);
}
else
{
M3D point_crossmat = crossmat_list[idx+j+1];
V3D C(s.rot.conjugate().normalized() * norm_vec);
V3D A(point_crossmat * C);
// V3D A(point_crossmat * state.rot_end.transpose() * norm_vec);
ekfom_data.h_x.block<1, 12>(m, 0) << norm_vec(0), norm_vec(1), norm_vec(2), VEC_FROM_ARRAY(A), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0;
}
ekfom_data.z(m) = -norm_vec(0) * feats_down_world->points[idx+j+1].x -norm_vec(1) * feats_down_world->points[idx+j+1].y -norm_vec(2) * feats_down_world->points[idx+j+1].z-normvec->points[j].intensity;
m++;
}
}
effct_feat_num += effect_num_k;
}
void h_model_IMU_output(state_output &s, esekfom::dyn_share_modified<double> &ekfom_data)
{
std::memset(ekfom_data.satu_check, false, 6);
ekfom_data.z_IMU.block<3,1>(0, 0) = angvel_avr - s.omg - s.bg;
ekfom_data.z_IMU.block<3,1>(3, 0) = acc_avr * G_m_s2 / acc_norm - s.acc - s.ba;
ekfom_data.R_IMU << imu_meas_omg_cov, imu_meas_omg_cov, imu_meas_omg_cov, imu_meas_acc_cov, imu_meas_acc_cov, imu_meas_acc_cov;
if(check_satu)
{
if(fabs(angvel_avr(0)) >= 0.99 * satu_gyro)
{
ekfom_data.satu_check[0] = true;
ekfom_data.z_IMU(0) = 0.0;
}
if(fabs(angvel_avr(1)) >= 0.99 * satu_gyro)
{
ekfom_data.satu_check[1] = true;
ekfom_data.z_IMU(1) = 0.0;
}
if(fabs(angvel_avr(2)) >= 0.99 * satu_gyro)
{
ekfom_data.satu_check[2] = true;
ekfom_data.z_IMU(2) = 0.0;
}
if(fabs(acc_avr(0)) >= 0.99 * satu_acc)
{
ekfom_data.satu_check[3] = true;
ekfom_data.z_IMU(3) = 0.0;
}
if(fabs(acc_avr(1)) >= 0.99 * satu_acc)
{
ekfom_data.satu_check[4] = true;
ekfom_data.z_IMU(4) = 0.0;
}
if(fabs(acc_avr(2)) >= 0.99 * satu_acc)
{
ekfom_data.satu_check[5] = true;
ekfom_data.z_IMU(5) = 0.0;
}
}
}
void pointBodyToWorld(PointType const * const pi, PointType * const po)
{
V3D p_body(pi->x, pi->y, pi->z);
V3D p_global;
if (extrinsic_est_en)
{
if (!use_imu_as_input)
{
p_global = kf_output.x_.rot.normalized() * (kf_output.x_.offset_R_L_I.normalized() * p_body + kf_output.x_.offset_T_L_I) + kf_output.x_.pos;
}
else
{
p_global = kf_input.x_.rot.normalized() * (kf_input.x_.offset_R_L_I.normalized() * p_body + kf_input.x_.offset_T_L_I) + kf_input.x_.pos;
}
}
else
{
if (!use_imu_as_input)
{
p_global = kf_output.x_.rot.normalized() * (Lidar_R_wrt_IMU * p_body + Lidar_T_wrt_IMU) + kf_output.x_.pos;
}
else
{
p_global = kf_input.x_.rot.normalized() * (Lidar_R_wrt_IMU * p_body + Lidar_T_wrt_IMU) + kf_input.x_.pos;
}
}
po->x = p_global(0);
po->y = p_global(1);
po->z = p_global(2);
po->intensity = pi->intensity;
}
const bool time_list(PointType &x, PointType &y) {return (x.curvature < y.curvature);};
point_lio_ros2/src/Estimator.h
-118
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@@ -1,118 +0,0 @@
#ifndef Estimator_H
#define Estimator_H
#include <../include/IKFoM/IKFoM_toolkit/esekfom/esekfom.hpp>
#include "common_lib.h"
#include "parameters.h"
#include <pcl_conversions/pcl_conversions.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>
#include <ikd-Tree/ikd_Tree.h>
#include <pcl/io/pcd_io.h>
extern PointCloudXYZI::Ptr normvec; //(new PointCloudXYZI(100000, 1));
extern std::vector<int> time_seq;
extern PointCloudXYZI::Ptr feats_down_body; //(new PointCloudXYZI());
extern PointCloudXYZI::Ptr feats_down_world; //(new PointCloudXYZI());
extern std::vector<V3D> pbody_list;
extern std::vector<PointVector> Nearest_Points;
extern KD_TREE<PointType> ikdtree;
extern std::vector<float> pointSearchSqDis;
extern bool point_selected_surf[100000]; // = {0};
extern std::vector<M3D> crossmat_list;
extern int effct_feat_num;
extern int k;
extern int idx;
extern V3D angvel_avr, acc_avr;
extern V3D Lidar_T_wrt_IMU; //(Zero3d);
extern M3D Lidar_R_wrt_IMU; //(Eye3d);
typedef MTK::vect<3, double> vect3;
typedef MTK::SO3<double> SO3;
typedef MTK::S2<double, 98090, 10000, 1> S2;
typedef MTK::vect<1, double> vect1;
typedef MTK::vect<2, double> vect2;
MTK_BUILD_MANIFOLD(state_input,
((vect3, pos))
((SO3, rot))
((SO3, offset_R_L_I))
((vect3, offset_T_L_I))
((vect3, vel))
((vect3, bg))
((vect3, ba))
((vect3, gravity))
);
MTK_BUILD_MANIFOLD(state_output,
((vect3, pos))
((SO3, rot))
((SO3, offset_R_L_I))
((vect3, offset_T_L_I))
((vect3, vel))
((vect3, omg))
((vect3, acc))
((vect3, gravity))
((vect3, bg))
((vect3, ba))
);
MTK_BUILD_MANIFOLD(input_ikfom,
((vect3, acc))
((vect3, gyro))
);
MTK_BUILD_MANIFOLD(process_noise_input,
((vect3, ng))
((vect3, na))
((vect3, nbg))
((vect3, nba))
);
MTK_BUILD_MANIFOLD(process_noise_output,
((vect3, vel))
((vect3, ng))
((vect3, na))
((vect3, nbg))
((vect3, nba))
);
extern esekfom::esekf<state_input, 24, input_ikfom> kf_input;
extern esekfom::esekf<state_output, 30, input_ikfom> kf_output;
extern state_input state_in;
extern state_output state_out;
extern input_ikfom input_in;
Eigen::Matrix<double, 24, 24> process_noise_cov_input();
Eigen::Matrix<double, 30, 30> process_noise_cov_output();
//double L_offset_to_I[3] = {0.04165, 0.02326, -0.0284}; // Avia
//vect3 Lidar_offset_to_IMU(L_offset_to_I, 3);
Eigen::Matrix<double, 24, 1> get_f_input(state_input &s, const input_ikfom &in);
Eigen::Matrix<double, 30, 1> get_f_output(state_output &s, const input_ikfom &in);
Eigen::Matrix<double, 24, 24> df_dx_input(state_input &s, const input_ikfom &in);
// Eigen::Matrix<double, 24, 12> df_dw_input(state_input &s, const input_ikfom &in);
Eigen::Matrix<double, 30, 30> df_dx_output(state_output &s, const input_ikfom &in);
// Eigen::Matrix<double, 30, 15> df_dw_output(state_output &s);
vect3 SO3ToEuler(const SO3 &orient);
void h_model_input(state_input &s, esekfom::dyn_share_modified<double> &ekfom_data);
void h_model_output(state_output &s, esekfom::dyn_share_modified<double> &ekfom_data);
void h_model_IMU_output(state_output &s, esekfom::dyn_share_modified<double> &ekfom_data);
void pointBodyToWorld(PointType const *const pi, PointType *const po);
const bool time_list(PointType &x, PointType &y); // {return (x.curvature < y.curvature);};
#endif
point_lio_ros2/src/IMU_Processing.hpp
-164
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@@ -1,164 +0,0 @@
#include <cmath>
#include <math.h>
#include <deque>
#include <mutex>
#include <thread>
#include <fstream>
#include <csignal>
#include <rclcpp/rclcpp.hpp>
#include <so3_math.h>
#include <Eigen/Eigen>
#include <common_lib.h>
#include <pcl/common/io.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <condition_variable>
#include <nav_msgs/msg/odometry.hpp>
#include <pcl/common/transforms.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <tf2_ros/transform_broadcaster.h>
#include <pcl_conversions/pcl_conversions.h>
#include <sensor_msgs/msg/imu.hpp>
#include <sensor_msgs/msg/point_cloud2.hpp>
#include <geometry_msgs/msg/vector3.hpp>
/// *************Preconfiguration
#define MAX_INI_COUNT (100)
/// *************IMU Process and undistortion
class ImuProcess {
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
ImuProcess();
~ImuProcess();
void Reset();
//void Reset(double start_timestamp, const sensor_msgs::msg::Imu::ConstSharedPtr &lastimu);
void Process(const MeasureGroup &meas, const PointCloudXYZI::Ptr &pcl_un_);
void Set_init(Eigen::Vector3d &tmp_gravity, Eigen::Matrix3d &rot);
ofstream fout_imu;
// double first_lidar_time;
int lidar_type;
bool imu_en;
V3D mean_acc, gravity_;
bool imu_need_init_ = true;
bool b_first_frame_ = true;
bool gravity_align_ = false;
private:
void IMU_init(const MeasureGroup &meas, int &N);
V3D mean_gyr;
int init_iter_num = 1;
rclcpp::Logger logger;
};
ImuProcess::ImuProcess()
: b_first_frame_(true), imu_need_init_(true), gravity_align_(false),
logger(rclcpp::get_logger("laserMapping")) {
imu_en = true;
init_iter_num = 1;
mean_acc = V3D(0, 0, -1.0);
mean_gyr = V3D(0, 0, 0);
}
ImuProcess::~ImuProcess() {}
void ImuProcess::Reset() {
RCLCPP_WARN(logger, "Reset ImuProcess");
mean_acc = V3D(0, 0, -1.0);
mean_gyr = V3D(0, 0, 0);
imu_need_init_ = true;
init_iter_num = 1;
}
void ImuProcess::IMU_init(const MeasureGroup &meas, int &N) {
/** 1. initializing the gravity, gyro bias, acc and gyro covariance
** 2. normalize the acceleration measurenments to unit gravity **/
RCLCPP_INFO(logger, "IMU Initializing: %.1f %%", double(N) / MAX_INI_COUNT * 100);
V3D cur_acc, cur_gyr;
if (b_first_frame_) {
Reset();
N = 1;
b_first_frame_ = false;
const auto &imu_acc = meas.imu.front()->linear_acceleration;
const auto &gyr_acc = meas.imu.front()->angular_velocity;
mean_acc << imu_acc.x, imu_acc.y, imu_acc.z;
mean_gyr << gyr_acc.x, gyr_acc.y, gyr_acc.z;
}
for (const auto &imu: meas.imu) {
const auto &imu_acc = imu->linear_acceleration;
const auto &gyr_acc = imu->angular_velocity;
cur_acc << imu_acc.x, imu_acc.y, imu_acc.z;
cur_gyr << gyr_acc.x, gyr_acc.y, gyr_acc.z;
mean_acc += (cur_acc - mean_acc) / N;
mean_gyr += (cur_gyr - mean_gyr) / N;
N++;
}
}
void ImuProcess::Process(const MeasureGroup &meas, const PointCloudXYZI::Ptr &cur_pcl_un_) {
if (imu_en) {
if (meas.imu.empty()) return;
assert(meas.lidar != nullptr);
if (imu_need_init_) {
/// The very first lidar frame
IMU_init(meas, init_iter_num);
imu_need_init_ = true;
if (init_iter_num > MAX_INI_COUNT) {
RCLCPP_INFO(logger, "IMU Initializing: %.1f %%", 100.0);
imu_need_init_ = false;
*cur_pcl_un_ = *(meas.lidar);
}
return;
}
if (!gravity_align_) gravity_align_ = true;
*cur_pcl_un_ = *(meas.lidar);
return;
} else {
if (!b_first_frame_) { if (!gravity_align_) gravity_align_ = true; }
else {
b_first_frame_ = false;
return;
}
*cur_pcl_un_ = *(meas.lidar);
return;
}
}
void ImuProcess::Set_init(Eigen::Vector3d &tmp_gravity, Eigen::Matrix3d &rot) {
/** 1. initializing the gravity, gyro bias, acc and gyro covariance
** 2. normalize the acceleration measurenments to unit gravity **/
// V3D tmp_gravity = - mean_acc / mean_acc.norm() * G_m_s2; // state_gravity;
M3D hat_grav;
hat_grav << 0.0, gravity_(2), -gravity_(1),
-gravity_(2), 0.0, gravity_(0),
gravity_(1), -gravity_(0), 0.0;
double align_norm = (hat_grav * tmp_gravity).norm() / tmp_gravity.norm() / gravity_.norm();
double align_cos = gravity_.transpose() * tmp_gravity;
align_cos = align_cos / gravity_.norm() / tmp_gravity.norm();
if (align_norm < 1e-6) {
if (align_cos > 1e-6) {
rot = Eye3d;
} else {
rot = -Eye3d;
}
} else {
V3D align_angle = hat_grav * tmp_gravity / (hat_grav * tmp_gravity).norm() * acos(align_cos);
rot = Exp(align_angle(0), align_angle(1), align_angle(2));
}
}
point_lio_ros2/src/laserMapping.cpp
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point_lio_ros2/src/parameters.cpp
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#include "parameters.h"
bool odom_only;
std::string odom_header_frame_id, odom_child_frame_id;
bool is_first_frame = true;
double lidar_end_time = 0.0, first_lidar_time = 0.0, time_con = 0.0;
double last_timestamp_lidar = -1.0, last_timestamp_imu = -1.0;
int pcd_index = 0;
std::string lid_topic, imu_topic;
bool prop_at_freq_of_imu, check_satu, con_frame, cut_frame;
bool use_imu_as_input, space_down_sample, publish_odometry_without_downsample;
int init_map_size, con_frame_num;
double match_s, satu_acc, satu_gyro, cut_frame_time_interval;
float plane_thr;
double filter_size_surf_min, filter_size_map_min, fov_deg;
double cube_len;
float DET_RANGE;
bool imu_en, gravity_align, non_station_start;
double imu_time_inte;
double laser_point_cov, acc_norm;
double vel_cov, acc_cov_input, gyr_cov_input;
double gyr_cov_output, acc_cov_output, b_gyr_cov, b_acc_cov;
double imu_meas_acc_cov, imu_meas_omg_cov;
int lidar_type, pcd_save_interval;
std::vector<double> gravity_init, gravity;
std::vector<double> extrinT;
std::vector<double> extrinR;
bool runtime_pos_log, pcd_save_en, path_en, extrinsic_est_en = true;
bool scan_pub_en, scan_body_pub_en;
shared_ptr<Preprocess> p_pre;
double time_lag_imu_to_lidar = 0.0;
void readParameters(shared_ptr<rclcpp::Node> &nh) {
p_pre.reset(new Preprocess());
nh->declare_parameter<bool>("odom_only", false);
nh->declare_parameter<std::string>("odom_header_frame_id", "camera_init");
nh->declare_parameter<std::string>("odom_child_frame_id", "aft_mapped");
nh->declare_parameter<bool>("prop_at_freq_of_imu", true);
nh->declare_parameter<bool>("use_imu_as_input", true);
nh->declare_parameter<bool>("check_satu", true);
nh->declare_parameter<int>("init_map_size", 100);
nh->declare_parameter<bool>("space_down_sample", true);
nh->declare_parameter<double>("mapping.satu_acc", 3.0);
nh->declare_parameter<double>("mapping.satu_gyro", 35.0);
nh->declare_parameter<double>("mapping.acc_norm", 1.0);
nh->declare_parameter<float>("mapping.plane_thr", 0.05f);
nh->declare_parameter<int>("point_filter_num", 2);
nh->declare_parameter<std::string>("common.lid_topic", "/livox/lidar");
nh->declare_parameter<std::string>("common.imu_topic", "/livox/imu");
nh->declare_parameter<bool>("common.con_frame", false);
nh->declare_parameter<int>("common.con_frame_num", 1);
nh->declare_parameter<bool>("common.cut_frame", false);
nh->declare_parameter<double>("common.cut_frame_time_interval", 0.1);
nh->declare_parameter<double>("common.time_lag_imu_to_lidar", 0.0);
nh->declare_parameter<double>("filter_size_surf", 0.5);
nh->declare_parameter<double>("filter_size_map", 0.5);
nh->declare_parameter<double>("cube_side_length", 200);
nh->declare_parameter<float>("mapping.det_range", 300.f);
nh->declare_parameter<double>("mapping.fov_degree", 180);
nh->declare_parameter<bool>("mapping.imu_en", true);
nh->declare_parameter<bool>("mapping.start_in_aggressive_motion", false);
nh->declare_parameter<bool>("mapping.extrinsic_est_en", true);
nh->declare_parameter<double>("mapping.imu_time_inte", 0.005);
nh->declare_parameter<double>("mapping.lidar_meas_cov", 0.1);
nh->declare_parameter<double>("mapping.acc_cov_input", 0.1);
nh->declare_parameter<double>("mapping.vel_cov", 20);
nh->declare_parameter<double>("mapping.gyr_cov_input", 0.1);
nh->declare_parameter<double>("mapping.gyr_cov_output", 0.1);
nh->declare_parameter<double>("mapping.acc_cov_output", 0.1);
nh->declare_parameter<double>("mapping.b_gyr_cov", 0.0001);
nh->declare_parameter<double>("mapping.b_acc_cov", 0.0001);
nh->declare_parameter<double>("mapping.imu_meas_acc_cov", 0.1);
nh->declare_parameter<double>("mapping.imu_meas_omg_cov", 0.1);
nh->declare_parameter<double>("preprocess.blind", 1.0);
nh->declare_parameter<int>("preprocess.lidar_type", 1);
nh->declare_parameter<int>("preprocess.scan_line", 16);
nh->declare_parameter<int>("preprocess.scan_rate", 10);
nh->declare_parameter<int>("preprocess.timestamp_unit", 1);
nh->declare_parameter<double>("mapping.match_s", 81);
nh->declare_parameter<bool>("mapping.gravity_align", true);
nh->declare_parameter<std::vector<double>>("mapping.gravity", {0, 0, -9.810});
nh->declare_parameter<std::vector<double>>("mapping.gravity_init", {0, 0, -9.810});
nh->declare_parameter<std::vector<double>>("mapping.extrinsic_T", {0, 0, 0});
nh->declare_parameter<std::vector<double>>("mapping.extrinsic_R", {1, 0, 0, 0, 1, 0, 0, 0, 1});
nh->declare_parameter<bool>("odometry.publish_odometry_without_downsample", false);
nh->declare_parameter<bool>("publish.path_en", true);
nh->declare_parameter<bool>("publish.scan_publish_en", true);
nh->declare_parameter<bool>("publish.scan_bodyframe_pub_en", true);
nh->declare_parameter<bool>("runtime_pos_log_enable", false);
nh->declare_parameter<bool>("pcd_save.pcd_save_en", false);
nh->declare_parameter<int>("pcd_save.interval", -1);
// 使用get_parameter方法获取参数值
nh->get_parameter("odom_only", odom_only);
nh->get_parameter("odom_header_frame_id", odom_header_frame_id);
nh->get_parameter("odom_child_frame_id", odom_child_frame_id);
nh->get_parameter("prop_at_freq_of_imu", prop_at_freq_of_imu);
nh->get_parameter("use_imu_as_input", use_imu_as_input);
nh->get_parameter("check_satu", check_satu);
nh->get_parameter("init_map_size", init_map_size);
nh->get_parameter("space_down_sample", space_down_sample);
nh->get_parameter("mapping.satu_acc", satu_acc);
nh->get_parameter("mapping.satu_gyro", satu_gyro);
nh->get_parameter("mapping.acc_norm", acc_norm);
nh->get_parameter("mapping.plane_thr", plane_thr);
nh->get_parameter("point_filter_num", p_pre->point_filter_num);
nh->get_parameter("common.lid_topic", lid_topic);
nh->get_parameter("common.imu_topic", imu_topic);
nh->get_parameter("common.con_frame", con_frame);
nh->get_parameter("common.con_frame_num", con_frame_num);
nh->get_parameter("common.cut_frame", cut_frame);
nh->get_parameter("common.cut_frame_time_interval", cut_frame_time_interval);
nh->get_parameter("common.time_lag_imu_to_lidar", time_lag_imu_to_lidar);
nh->get_parameter("filter_size_surf", filter_size_surf_min);
nh->get_parameter("filter_size_map", filter_size_map_min);
nh->get_parameter("cube_side_length", cube_len);
nh->get_parameter("mapping.det_range", DET_RANGE);
nh->get_parameter("mapping.fov_degree", fov_deg);
nh->get_parameter("mapping.imu_en", imu_en);
nh->get_parameter("mapping.start_in_aggressive_motion", non_station_start);
nh->get_parameter("mapping.extrinsic_est_en", extrinsic_est_en);
nh->get_parameter("mapping.imu_time_inte", imu_time_inte);
nh->get_parameter("mapping.lidar_meas_cov", laser_point_cov);
nh->get_parameter("mapping.acc_cov_input", acc_cov_input);
nh->get_parameter("mapping.vel_cov", vel_cov);
nh->get_parameter("mapping.gyr_cov_input", gyr_cov_input);
nh->get_parameter("mapping.gyr_cov_output", gyr_cov_output);
nh->get_parameter("mapping.acc_cov_output", acc_cov_output);
nh->get_parameter("mapping.b_gyr_cov", b_gyr_cov);
nh->get_parameter("mapping.b_acc_cov", b_acc_cov);
nh->get_parameter("mapping.imu_meas_acc_cov", imu_meas_acc_cov);
nh->get_parameter("mapping.imu_meas_omg_cov", imu_meas_omg_cov);
nh->get_parameter("preprocess.blind", p_pre->blind);
nh->get_parameter("preprocess.lidar_type", lidar_type);
nh->get_parameter("preprocess.scan_line", p_pre->N_SCANS);
nh->get_parameter("preprocess.scan_rate", p_pre->SCAN_RATE);
nh->get_parameter("preprocess.timestamp_unit", p_pre->time_unit);
nh->get_parameter("mapping.match_s", match_s);
nh->get_parameter("mapping.gravity_align", gravity_align);
nh->get_parameter("mapping.gravity", gravity);
nh->get_parameter("mapping.gravity_init", gravity_init);
nh->get_parameter("mapping.extrinsic_T", extrinT);
nh->get_parameter("mapping.extrinsic_R", extrinR);
nh->get_parameter("odometry.publish_odometry_without_downsample", publish_odometry_without_downsample);
nh->get_parameter("publish.path_en", path_en);
nh->get_parameter("publish.scan_publish_en", scan_pub_en);
nh->get_parameter("publish.scan_bodyframe_pub_en", scan_body_pub_en);
nh->get_parameter("runtime_pos_log_enable", runtime_pos_log);
nh->get_parameter("pcd_save.pcd_save_en", pcd_save_en);
nh->get_parameter("pcd_save.interval", pcd_save_interval);
}
point_lio_ros2/src/parameters.h
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// #ifndef PARAM_H
// #define PARAM_H
#pragma once
#include <rclcpp/rclcpp.hpp>
#include <Eigen/Eigen>
#include <Eigen/Core>
#include <cstring>
#include <string>
#include "preprocess.h"
extern bool odom_only;
extern std::string odom_header_frame_id;
extern std::string odom_child_frame_id;
extern bool is_first_frame;
extern double lidar_end_time, first_lidar_time, time_con;
extern double last_timestamp_lidar, last_timestamp_imu;
extern int pcd_index;
extern std::string lid_topic, imu_topic;
extern bool prop_at_freq_of_imu, check_satu, con_frame, cut_frame;
extern bool use_imu_as_input, space_down_sample;
extern bool extrinsic_est_en, publish_odometry_without_downsample;
extern int init_map_size, con_frame_num;
extern double match_s, satu_acc, satu_gyro, cut_frame_time_interval;
extern float plane_thr;
extern double filter_size_surf_min, filter_size_map_min, fov_deg;
extern double cube_len;
extern float DET_RANGE;
extern bool imu_en, gravity_align, non_station_start;
extern double imu_time_inte;
extern double laser_point_cov, acc_norm;
extern double acc_cov_input, gyr_cov_input, vel_cov;
extern double gyr_cov_output, acc_cov_output, b_gyr_cov, b_acc_cov;
extern double imu_meas_acc_cov, imu_meas_omg_cov;
extern int lidar_type, pcd_save_interval;
extern std::vector<double> gravity_init, gravity;
extern std::vector<double> extrinT;
extern std::vector<double> extrinR;
extern bool runtime_pos_log, pcd_save_en, path_en;
extern bool scan_pub_en, scan_body_pub_en;
extern shared_ptr<Preprocess> p_pre;
extern double time_lag_imu_to_lidar;
void readParameters(shared_ptr<rclcpp::Node> &nh);
point_lio_ros2/src/preprocess.cpp
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@@ -1,732 +0,0 @@
#include "preprocess.h"
#define RETURN0 0x00
#define RETURN0AND1 0x10
Preprocess::Preprocess()
: lidar_type(AVIA), blind(0.01), point_filter_num(1) {
inf_bound = 10;
N_SCANS = 6;
SCAN_RATE = 10;
group_size = 8;
disA = 0.01;
disA = 0.1; // B?
p2l_ratio = 225;
limit_maxmid = 6.25;
limit_midmin = 6.25;
limit_maxmin = 3.24;
jump_up_limit = 170.0;
jump_down_limit = 8.0;
cos160 = 160.0;
edgea = 2;
edgeb = 0.1;
smallp_intersect = 172.5;
smallp_ratio = 1.2;
given_offset_time = false;
jump_up_limit = cos(jump_up_limit / 180 * M_PI);
jump_down_limit = cos(jump_down_limit / 180 * M_PI);
cos160 = cos(cos160 / 180 * M_PI);
smallp_intersect = cos(smallp_intersect / 180 * M_PI);
}
Preprocess::~Preprocess() {}
void Preprocess::set(bool feat_en, int lid_type, double bld, int pfilt_num) {
lidar_type = lid_type;
blind = bld;
point_filter_num = pfilt_num;
}
// void Preprocess::process(const livox_ros_driver2::msg::CustomMsg::SharedPtr &msg, PointCloudXYZI::Ptr &pcl_out) {
// avia_handler(msg);
// *pcl_out = pl_surf;
// }
void Preprocess::process(const sensor_msgs::msg::PointCloud2::SharedPtr &msg, PointCloudXYZI::Ptr &pcl_out) {
switch (time_unit) {
case SEC:
time_unit_scale = 1.e3f;
break;
case MS:
time_unit_scale = 1.f;
break;
case US:
time_unit_scale = 1.e-3f;
break;
case NS:
time_unit_scale = 1.e-6f;
break;
default:
time_unit_scale = 1.f;
break;
}
switch (lidar_type) {
case OUST64:
oust64_handler(msg);
break;
case VELO16:
velodyne_handler(msg);
break;
case HESAIxt32:
hesai_handler(msg);
break;
case UNILIDAR:
unilidar_handler(msg);
break;
default:
printf("Error LiDAR Type");
break;
}
*pcl_out = pl_surf;
}
// void Preprocess::avia_handler(const livox_ros_driver2::msg::CustomMsg::SharedPtr &msg) {
// pl_surf.clear();
// pl_corn.clear();
// pl_full.clear();
// double t1 = omp_get_wtime();
// int plsize = msg->point_num;
// pl_corn.reserve(plsize);
// pl_surf.reserve(plsize);
// pl_full.resize(plsize);
// uint valid_num = 0;
// for (uint i = 1; i < plsize; i++) {
// if ((msg->points[i].line < N_SCANS) &&
// ((msg->points[i].tag & 0x30) == 0x10 || (msg->points[i].tag & 0x30) == 0x00)) {
// valid_num++;
// if (valid_num % point_filter_num == 0) {
// pl_full[i].x = msg->points[i].x;
// pl_full[i].y = msg->points[i].y;
// pl_full[i].z = msg->points[i].z;
// pl_full[i].intensity = msg->points[i].reflectivity;
// pl_full[i].curvature = msg->points[i].offset_time /
// float(1000000); // use curvature as time of each laser points, curvature unit: ms
// if (i == 0) pl_full[i].curvature = fabs(pl_full[i].curvature) < 1.0 ? pl_full[i].curvature : 0.0;
// else pl_full[i].curvature =
// fabs(pl_full[i].curvature - pl_full[i - 1].curvature) < 1.0 ? pl_full[i].curvature :
// pl_full[i - 1].curvature + 0.004166667f;
// if ((abs(pl_full[i].x - pl_full[i - 1].x) > 1e-7)
// || (abs(pl_full[i].y - pl_full[i - 1].y) > 1e-7)
// || (abs(pl_full[i].z - pl_full[i - 1].z) > 1e-7)
// && (pl_full[i].x * pl_full[i].x + pl_full[i].y * pl_full[i].y + pl_full[i].z * pl_full[i].z >
// (blind * blind))) {
// pl_surf.push_back(pl_full[i]);
// }
// }
// }
// }
// }
void Preprocess::oust64_handler(const sensor_msgs::msg::PointCloud2::SharedPtr &msg) {
pl_surf.clear();
pl_corn.clear();
pl_full.clear();
pcl::PointCloud<ouster_ros::Point> pl_orig;
pcl::fromROSMsg(*msg, pl_orig);
int plsize = pl_orig.size();
pl_corn.reserve(plsize);
pl_surf.reserve(plsize);
double time_stamp = rclcpp::Time(msg->header.stamp).seconds();
// cout << "===================================" << endl;
// printf("Pt size = %d, N_SCANS = %d\r\n", plsize, N_SCANS);
for (int i = 0; i < pl_orig.points.size(); i++) {
if (i % point_filter_num != 0) continue;
double range = pl_orig.points[i].x * pl_orig.points[i].x + pl_orig.points[i].y * pl_orig.points[i].y +
pl_orig.points[i].z * pl_orig.points[i].z;
if (range < (blind * blind)) continue;
Eigen::Vector3d pt_vec;
PointType added_pt;
added_pt.x = pl_orig.points[i].x;
added_pt.y = pl_orig.points[i].y;
added_pt.z = pl_orig.points[i].z;
added_pt.intensity = pl_orig.points[i].intensity;
added_pt.normal_x = 0;
added_pt.normal_y = 0;
added_pt.normal_z = 0;
added_pt.curvature = pl_orig.points[i].t * time_unit_scale; // curvature unit: ms
pl_surf.points.push_back(added_pt);
}
// pub_func(pl_surf, pub_full, msg->header.stamp);
// pub_func(pl_surf, pub_corn, msg->header.stamp);
}
void Preprocess::velodyne_handler(const sensor_msgs::msg::PointCloud2::SharedPtr &msg) {
pl_surf.clear();
pl_corn.clear();
pl_full.clear();
pcl::PointCloud<velodyne_ros::Point> pl_orig;
pcl::fromROSMsg(*msg, pl_orig);
int plsize = pl_orig.points.size();
if (plsize == 0) return;
pl_surf.reserve(plsize);
/*** These variables only works when no point timestamps given ***/
double omega_l = 0.361 * SCAN_RATE; // scan angular velocity
std::vector<bool> is_first(N_SCANS, true);
std::vector<double> yaw_fp(N_SCANS, 0.0); // yaw of first scan point
std::vector<float> yaw_last(N_SCANS, 0.0); // yaw of last scan point
std::vector<float> time_last(N_SCANS, 0.0); // last offset time
/*****************************************************************/
if (pl_orig.points[plsize - 1].time > 0) {
given_offset_time = true;
} else {
given_offset_time = false;
double yaw_first = atan2(pl_orig.points[0].y, pl_orig.points[0].x) * 57.29578;
double yaw_end = yaw_first;
int layer_first = pl_orig.points[0].ring;
for (uint i = plsize - 1; i > 0; i--) {
if (pl_orig.points[i].ring == layer_first) {
yaw_end = atan2(pl_orig.points[i].y, pl_orig.points[i].x) * 57.29578;
break;
}
}
}
for (int i = 0; i < plsize; i++) {
PointType added_pt;
// cout<<"!!!!!!"<<i<<" "<<plsize<<endl;
added_pt.normal_x = 0;
added_pt.normal_y = 0;
added_pt.normal_z = 0;
added_pt.x = pl_orig.points[i].x;
added_pt.y = pl_orig.points[i].y;
added_pt.z = pl_orig.points[i].z;
added_pt.intensity = pl_orig.points[i].intensity;
added_pt.curvature = pl_orig.points[i].time * time_unit_scale; // curvature unit: ms // cout<<added_pt.curvature<<endl;
if (!given_offset_time) {
int layer = pl_orig.points[i].ring;
double yaw_angle = atan2(added_pt.y, added_pt.x) * 57.2957;
if (is_first[layer]) {
// printf("layer: %d; is first: %d", layer, is_first[layer]);
yaw_fp[layer] = yaw_angle;
is_first[layer] = false;
added_pt.curvature = 0.0;
yaw_last[layer] = yaw_angle;
time_last[layer] = added_pt.curvature;
continue;
}
// compute offset time
if (yaw_angle <= yaw_fp[layer]) {
added_pt.curvature = (yaw_fp[layer] - yaw_angle) / omega_l;
} else {
added_pt.curvature = (yaw_fp[layer] - yaw_angle + 360.0) / omega_l;
}
if (added_pt.curvature < time_last[layer]) added_pt.curvature += 360.0 / omega_l;
yaw_last[layer] = yaw_angle;
time_last[layer] = added_pt.curvature;
}
if (i % point_filter_num == 0) {
if (added_pt.x * added_pt.x
+ added_pt.y * added_pt.y
+ added_pt.z * added_pt.z > (blind * blind))
{
pl_surf.points.push_back(added_pt);
}
}
}
}
void Preprocess::unilidar_handler(const sensor_msgs::msg::PointCloud2::SharedPtr &msg)
{
pl_surf.clear();
pl_corn.clear();
pl_full.clear();
pcl::PointCloud<unilidar_ros::Point> pl_orig;
pcl::fromROSMsg(*msg, pl_orig);
int plsize = pl_orig.points.size();
if (plsize == 0) return;
pl_surf.reserve(plsize);
// std::cout << "plsize = " << plsize << ", given_offset_time = " << given_offset_time << std::endl;
int countElimnated = 0;
for (int i = 0; i < plsize; i++)
{
PointType added_pt;
added_pt.normal_x = 0;
added_pt.normal_y = 0;
added_pt.normal_z = 0;
added_pt.x = pl_orig.points[i].x;
added_pt.y = pl_orig.points[i].y;
added_pt.z = pl_orig.points[i].z;
added_pt.intensity = pl_orig.points[i].intensity;
added_pt.curvature = pl_orig.points[i].time * time_unit_scale;
if (added_pt.x * added_pt.x + added_pt.y * added_pt.y + added_pt.z * added_pt.z > (blind * blind))
{
pl_surf.points.push_back(added_pt);
}
else
{
countElimnated++;
}
}
// std::cout << "pl_surf.size() = " << pl_surf.size() << ", countElimnated = " << countElimnated << std::endl;
}
void Preprocess::hesai_handler(const sensor_msgs::msg::PointCloud2::SharedPtr &msg) {
pl_surf.clear();
pl_corn.clear();
pl_full.clear();
pcl::PointCloud<hesai_ros::Point> pl_orig;
pcl::fromROSMsg(*msg, pl_orig);
int plsize = pl_orig.points.size();
if (plsize == 0) return;
pl_surf.reserve(plsize);
/*** These variables only works when no point timestamps given ***/
double omega_l = 0.361 * SCAN_RATE; // scan angular velocity
std::vector<bool> is_first(N_SCANS, true);
std::vector<double> yaw_fp(N_SCANS, 0.0); // yaw of first scan point
std::vector<float> yaw_last(N_SCANS, 0.0); // yaw of last scan point
std::vector<float> time_last(N_SCANS, 0.0); // last offset time
/*****************************************************************/
if (pl_orig.points[plsize - 1].timestamp > 0) {
given_offset_time = true;
} else {
given_offset_time = false;
double yaw_first = atan2(pl_orig.points[0].y, pl_orig.points[0].x) * 57.29578;
double yaw_end = yaw_first;
int layer_first = pl_orig.points[0].ring;
for (uint i = plsize - 1; i > 0; i--) {
if (pl_orig.points[i].ring == layer_first) {
yaw_end = atan2(pl_orig.points[i].y, pl_orig.points[i].x) * 57.29578;
break;
}
}
}
double time_head = pl_orig.points[0].timestamp;
for (int i = 0; i < plsize; i++) {
PointType added_pt;
// cout<<"!!!!!!"<<i<<" "<<plsize<<endl;
added_pt.normal_x = 0;
added_pt.normal_y = 0;
added_pt.normal_z = 0;
added_pt.x = pl_orig.points[i].x;
added_pt.y = pl_orig.points[i].y;
added_pt.z = pl_orig.points[i].z;
added_pt.intensity = pl_orig.points[i].intensity;
added_pt.curvature = (pl_orig.points[i].timestamp - time_head) *
1000.f; // time_unit_scale; // curvature unit: ms // cout<<added_pt.curvature<<endl;
if (!given_offset_time) {
int layer = pl_orig.points[i].ring;
double yaw_angle = atan2(added_pt.y, added_pt.x) * 57.2957;
if (is_first[layer]) {
// printf("layer: %d; is first: %d", layer, is_first[layer]);
yaw_fp[layer] = yaw_angle;
is_first[layer] = false;
added_pt.curvature = 0.0;
yaw_last[layer] = yaw_angle;
time_last[layer] = added_pt.curvature;
continue;
}
// compute offset time
if (yaw_angle <= yaw_fp[layer]) {
added_pt.curvature = (yaw_fp[layer] - yaw_angle) / omega_l;
} else {
added_pt.curvature = (yaw_fp[layer] - yaw_angle + 360.0) / omega_l;
}
if (added_pt.curvature < time_last[layer]) added_pt.curvature += 360.0 / omega_l;
yaw_last[layer] = yaw_angle;
time_last[layer] = added_pt.curvature;
}
if (i % point_filter_num == 0) {
if (added_pt.x * added_pt.x + added_pt.y * added_pt.y + added_pt.z * added_pt.z > (blind * blind)) {
pl_surf.points.push_back(added_pt);
}
}
}
}
void Preprocess::give_feature(pcl::PointCloud<PointType> &pl, vector<orgtype> &types) {
int plsize = pl.size();
int plsize2;
if (plsize == 0) {
printf("something wrong\n");
return;
}
uint head = 0;
while (types[head].range < blind) {
head++;
}
// Surf
plsize2 = (plsize > group_size) ? (plsize - group_size) : 0;
Eigen::Vector3d curr_direct(Eigen::Vector3d::Zero());
Eigen::Vector3d last_direct(Eigen::Vector3d::Zero());
uint i_nex = 0, i2;
uint last_i = 0;
uint last_i_nex = 0;
int last_state = 0;
int plane_type;
for (uint i = head; i < plsize2; i++) {
if (types[i].range < blind) {
continue;
}
i2 = i;
plane_type = plane_judge(pl, types, i, i_nex, curr_direct);
if (plane_type == 1) {
for (uint j = i; j <= i_nex; j++) {
if (j != i && j != i_nex) {
types[j].ftype = Real_Plane;
} else {
types[j].ftype = Poss_Plane;
}
}
// if(last_state==1 && fabs(last_direct.sum())>0.5)
if (last_state == 1 && last_direct.norm() > 0.1) {
double mod = last_direct.transpose() * curr_direct;
if (mod > -0.707 && mod < 0.707) {
types[i].ftype = Edge_Plane;
} else {
types[i].ftype = Real_Plane;
}
}
i = i_nex - 1;
last_state = 1;
} else // if(plane_type == 2)
{
i = i_nex;
last_state = 0;
}
last_i = i2;
last_i_nex = i_nex;
last_direct = curr_direct;
}
plsize2 = plsize > 3 ? plsize - 3 : 0;
for (uint i = head + 3; i < plsize2; i++) {
if (types[i].range < blind || types[i].ftype >= Real_Plane) {
continue;
}
if (types[i - 1].dista < 1e-16 || types[i].dista < 1e-16) {
continue;
}
Eigen::Vector3d vec_a(pl[i].x, pl[i].y, pl[i].z);
Eigen::Vector3d vecs[2];
for (int j = 0; j < 2; j++) {
int m = -1;
if (j == 1) {
m = 1;
}
if (types[i + m].range < blind) {
if (types[i].range > inf_bound) {
types[i].edj[j] = Nr_inf;
} else {
types[i].edj[j] = Nr_blind;
}
continue;
}
vecs[j] = Eigen::Vector3d(pl[i + m].x, pl[i + m].y, pl[i + m].z);
vecs[j] = vecs[j] - vec_a;
types[i].angle[j] = vec_a.dot(vecs[j]) / vec_a.norm() / vecs[j].norm();
if (types[i].angle[j] < jump_up_limit) {
types[i].edj[j] = Nr_180;
} else if (types[i].angle[j] > jump_down_limit) {
types[i].edj[j] = Nr_zero;
}
}
types[i].intersect = vecs[Prev].dot(vecs[Next]) / vecs[Prev].norm() / vecs[Next].norm();
if (types[i].edj[Prev] == Nr_nor && types[i].edj[Next] == Nr_zero && types[i].dista > 0.0225 &&
types[i].dista > 4 * types[i - 1].dista) {
if (types[i].intersect > cos160) {
if (edge_jump_judge(pl, types, i, Prev)) {
types[i].ftype = Edge_Jump;
}
}
} else if (types[i].edj[Prev] == Nr_zero && types[i].edj[Next] == Nr_nor && types[i - 1].dista > 0.0225 &&
types[i - 1].dista > 4 * types[i].dista) {
if (types[i].intersect > cos160) {
if (edge_jump_judge(pl, types, i, Next)) {
types[i].ftype = Edge_Jump;
}
}
} else if (types[i].edj[Prev] == Nr_nor && types[i].edj[Next] == Nr_inf) {
if (edge_jump_judge(pl, types, i, Prev)) {
types[i].ftype = Edge_Jump;
}
} else if (types[i].edj[Prev] == Nr_inf && types[i].edj[Next] == Nr_nor) {
if (edge_jump_judge(pl, types, i, Next)) {
types[i].ftype = Edge_Jump;
}
} else if (types[i].edj[Prev] > Nr_nor && types[i].edj[Next] > Nr_nor) {
if (types[i].ftype == Nor) {
types[i].ftype = Wire;
}
}
}
plsize2 = plsize - 1;
double ratio;
for (uint i = head + 1; i < plsize2; i++) {
if (types[i].range < blind || types[i - 1].range < blind || types[i + 1].range < blind) {
continue;
}
if (types[i - 1].dista < 1e-8 || types[i].dista < 1e-8) {
continue;
}
if (types[i].ftype == Nor) {
if (types[i - 1].dista > types[i].dista) {
ratio = types[i - 1].dista / types[i].dista;
} else {
ratio = types[i].dista / types[i - 1].dista;
}
if (types[i].intersect < smallp_intersect && ratio < smallp_ratio) {
if (types[i - 1].ftype == Nor) {
types[i - 1].ftype = Real_Plane;
}
if (types[i + 1].ftype == Nor) {
types[i + 1].ftype = Real_Plane;
}
types[i].ftype = Real_Plane;
}
}
}
int last_surface = -1;
for (uint j = head; j < plsize; j++) {
if (types[j].ftype == Poss_Plane || types[j].ftype == Real_Plane) {
if (last_surface == -1) {
last_surface = j;
}
if (j == uint(last_surface + point_filter_num - 1)) {
PointType ap;
ap.x = pl[j].x;
ap.y = pl[j].y;
ap.z = pl[j].z;
ap.intensity = pl[j].intensity;
ap.curvature = pl[j].curvature;
pl_surf.push_back(ap);
last_surface = -1;
}
} else {
if (types[j].ftype == Edge_Jump || types[j].ftype == Edge_Plane) {
pl_corn.push_back(pl[j]);
}
if (last_surface != -1) {
PointType ap;
for (uint k = last_surface; k < j; k++) {
ap.x += pl[k].x;
ap.y += pl[k].y;
ap.z += pl[k].z;
ap.intensity += pl[k].intensity;
ap.curvature += pl[k].curvature;
}
ap.x /= (j - last_surface);
ap.y /= (j - last_surface);
ap.z /= (j - last_surface);
ap.intensity /= (j - last_surface);
ap.curvature /= (j - last_surface);
pl_surf.push_back(ap);
}
last_surface = -1;
}
}
}
void Preprocess::pub_func(PointCloudXYZI &pl, const rclcpp::Time &ct) {
pl.height = 1;
pl.width = pl.size();
sensor_msgs::msg::PointCloud2 output;
pcl::toROSMsg(pl, output);
output.header.frame_id = "livox";
output.header.stamp = ct;
}
int Preprocess::plane_judge(const PointCloudXYZI &pl, vector<orgtype> &types, uint i_cur, uint &i_nex,
Eigen::Vector3d &curr_direct) {
double group_dis = disA * types[i_cur].range + disB;
group_dis = group_dis * group_dis;
// i_nex = i_cur;
double two_dis;
vector<double> disarr;
disarr.reserve(20);
for (i_nex = i_cur; i_nex < i_cur + group_size; i_nex++) {
if (types[i_nex].range < blind) {
curr_direct.setZero();
return 2;
}
disarr.push_back(types[i_nex].dista);
}
for (;;) {
if ((i_cur >= pl.size()) || (i_nex >= pl.size())) break;
if (types[i_nex].range < blind) {
curr_direct.setZero();
return 2;
}
vx = pl[i_nex].x - pl[i_cur].x;
vy = pl[i_nex].y - pl[i_cur].y;
vz = pl[i_nex].z - pl[i_cur].z;
two_dis = vx * vx + vy * vy + vz * vz;
if (two_dis >= group_dis) {
break;
}
disarr.push_back(types[i_nex].dista);
i_nex++;
}
double leng_wid = 0;
double v1[3], v2[3];
for (uint j = i_cur + 1; j < i_nex; j++) {
if ((j >= pl.size()) || (i_cur >= pl.size())) break;
v1[0] = pl[j].x - pl[i_cur].x;
v1[1] = pl[j].y - pl[i_cur].y;
v1[2] = pl[j].z - pl[i_cur].z;
v2[0] = v1[1] * vz - vy * v1[2];
v2[1] = v1[2] * vx - v1[0] * vz;
v2[2] = v1[0] * vy - vx * v1[1];
double lw = v2[0] * v2[0] + v2[1] * v2[1] + v2[2] * v2[2];
if (lw > leng_wid) {
leng_wid = lw;
}
}
if ((two_dis * two_dis / leng_wid) < p2l_ratio) {
curr_direct.setZero();
return 0;
}
uint disarrsize = disarr.size();
for (uint j = 0; j < disarrsize - 1; j++) {
for (uint k = j + 1; k < disarrsize; k++) {
if (disarr[j] < disarr[k]) {
leng_wid = disarr[j];
disarr[j] = disarr[k];
disarr[k] = leng_wid;
}
}
}
if (disarr[disarr.size() - 2] < 1e-16) {
curr_direct.setZero();
return 0;
}
if (lidar_type == AVIA) {
double dismax_mid = disarr[0] / disarr[disarrsize / 2];
double dismid_min = disarr[disarrsize / 2] / disarr[disarrsize - 2];
if (dismax_mid >= limit_maxmid || dismid_min >= limit_midmin) {
curr_direct.setZero();
return 0;
}
} else {
double dismax_min = disarr[0] / disarr[disarrsize - 2];
if (dismax_min >= limit_maxmin) {
curr_direct.setZero();
return 0;
}
}
curr_direct << vx, vy, vz;
curr_direct.normalize();
return 1;
}
bool Preprocess::edge_jump_judge(const PointCloudXYZI &pl, vector<orgtype> &types, uint i, Surround nor_dir) {
if (nor_dir == 0) {
if (types[i - 1].range < blind || types[i - 2].range < blind) {
return false;
}
} else if (nor_dir == 1) {
if (types[i + 1].range < blind || types[i + 2].range < blind) {
return false;
}
}
double d1 = types[i + nor_dir - 1].dista;
double d2 = types[i + 3 * nor_dir - 2].dista;
double d;
if (d1 < d2) {
d = d1;
d1 = d2;
d2 = d;
}
d1 = sqrt(d1);
d2 = sqrt(d2);
if (d1 > edgea * d2 || (d1 - d2) > edgeb) {
return false;
}
return true;
}
point_lio_ros2/src/preprocess.h
-193
View File
@@ -1,193 +0,0 @@
#include <rclcpp/rclcpp.hpp>
#include <pcl_conversions/pcl_conversions.h>
#include <sensor_msgs/msg/point_cloud2.hpp>
// #include <livox_ros_driver2/msg/custom_msg.hpp>
using namespace std;
#define IS_VALID(a) ((abs(a)>1e8) ? true : false)
typedef pcl::PointXYZINormal PointType;
typedef pcl::PointCloud<PointType> PointCloudXYZI;
enum LID_TYPE {
AVIA = 1, VELO16, OUST64, HESAIxt32, UNILIDAR
}; //{1, 2, 3, 4}
enum TIME_UNIT {
SEC = 0, MS = 1, US = 2, NS = 3
};
enum Feature {
Nor, Poss_Plane, Real_Plane, Edge_Jump, Edge_Plane, Wire, ZeroPoint
};
enum Surround {
Prev, Next
};
enum E_jump {
Nr_nor, Nr_zero, Nr_180, Nr_inf, Nr_blind
};
const bool time_list_cut_frame(PointType &x, PointType &y);
struct orgtype {
double range;
double dista;
double angle[2];
double intersect;
E_jump edj[2];
Feature ftype;
orgtype() {
range = 0;
edj[Prev] = Nr_nor;
edj[Next] = Nr_nor;
ftype = Nor;
intersect = 2;
}
};
namespace velodyne_ros {
struct EIGEN_ALIGN16 Point {
PCL_ADD_POINT4D;
float intensity;
float time;
uint16_t ring;
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
} // namespace velodyne_ros
POINT_CLOUD_REGISTER_POINT_STRUCT(velodyne_ros::Point,
(float, x, x)
(float, y, y)
(float, z, z)
(float, intensity, intensity)
(float, time, time)
(std::uint16_t, ring, ring)
)
/**
* @brief Unilidar Point Type
*/
namespace unilidar_ros {
struct EIGEN_ALIGN16 Point {
PCL_ADD_POINT4D
PCL_ADD_INTENSITY
std::uint16_t ring;
float time;
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
} // namespace unilidar_ros
POINT_CLOUD_REGISTER_POINT_STRUCT(unilidar_ros::Point,
(float, x, x)
(float, y, y)
(float, z, z)
(float, intensity, intensity)
(std::uint16_t, ring, ring)
(float, time, time)
)
namespace hesai_ros {
struct EIGEN_ALIGN16 Point {
PCL_ADD_POINT4D;
float intensity;
double timestamp;
uint16_t ring;
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
} // namespace velodyne_ros
POINT_CLOUD_REGISTER_POINT_STRUCT(hesai_ros::Point,
(float, x, x)
(float, y, y)
(float, z, z)
(float, intensity, intensity)
(double, timestamp, timestamp)
(std::uint16_t, ring, ring)
)
namespace ouster_ros {
struct EIGEN_ALIGN16 Point {
PCL_ADD_POINT4D;
float intensity;
uint32_t t;
uint16_t reflectivity;
uint8_t ring;
uint16_t ambient;
uint32_t range;
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
};
} // namespace ouster_ros
// clang-format off
POINT_CLOUD_REGISTER_POINT_STRUCT(ouster_ros::Point,
(float, x, x)
(float, y, y)
(float, z, z)
(float, intensity, intensity)
// use std::uint32_t to avoid conflicting with pcl::uint32_t
(std::uint32_t, t, t)
(std::uint16_t, reflectivity, reflectivity)
(std::uint8_t, ring, ring)
(std::uint16_t, ambient, ambient)
(std::uint32_t, range, range)
)
class Preprocess {
public:
// EIGEN_MAKE_ALIGNED_OPERATOR_NEW
Preprocess();
~Preprocess();
// void process(const livox_ros_driver2::msg::CustomMsg::SharedPtr &msg, PointCloudXYZI::Ptr &pcl_out);
void process(const sensor_msgs::msg::PointCloud2::SharedPtr &msg, PointCloudXYZI::Ptr &pcl_out);
void set(bool feat_en, int lid_type, double bld, int pfilt_num);
// sensor_msgs::msg::PointCloud2::ConstSharedPtr pointcloud;
PointCloudXYZI pl_full, pl_corn, pl_surf;
PointCloudXYZI pl_buff[128]; //maximum 128 line lidar
vector<orgtype> typess[128]; //maximum 128 line lidar
float time_unit_scale;
int lidar_type, point_filter_num, N_SCANS, SCAN_RATE, time_unit;
double blind;
bool given_offset_time;
//ros::Publisher pub_full, pub_surf, pub_corn;
private:
// void avia_handler(const livox_ros_driver2::msg::CustomMsg::SharedPtr &msg);
void oust64_handler(const sensor_msgs::msg::PointCloud2::SharedPtr &msg);
void velodyne_handler(const sensor_msgs::msg::PointCloud2::SharedPtr &msg);
void unilidar_handler(const sensor_msgs::msg::PointCloud2::SharedPtr &msg);
void hesai_handler(const sensor_msgs::msg::PointCloud2::SharedPtr &msg);
void give_feature(PointCloudXYZI &pl, vector<orgtype> &types);
void pub_func(PointCloudXYZI &pl, const rclcpp::Time &ct);
int
plane_judge(const PointCloudXYZI &pl, vector<orgtype> &types, uint i, uint &i_nex, Eigen::Vector3d &curr_direct);
bool small_plane(const PointCloudXYZI &pl, vector<orgtype> &types, uint i_cur, uint &i_nex,
Eigen::Vector3d &curr_direct);
bool edge_jump_judge(const PointCloudXYZI &pl, vector<orgtype> &types, uint i, Surround nor_dir);
int group_size;
double disA, disB, inf_bound;
double limit_maxmid, limit_midmin, limit_maxmin;
double p2l_ratio;
double jump_up_limit, jump_down_limit;
double cos160;
double edgea, edgeb;
double smallp_intersect, smallp_ratio;
double vx, vy, vz;
};
upstream.jazzy.repos
+4
View File
@@ -11,6 +11,10 @@ repositories:
type: git
url: https://github.com/ros-navigation/navigation2.git
revision: jazzy
point_lio_ros2:
type: git
url: https://github.com/dfloreaa/point_lio_ros2.git
revision: main
teleop_twist_keyboard:
type: git
url: https://github.com/ros2/teleop_twist_keyboard.git