feat(slam): add rtabmap_ros

This commit is contained in:
X-lanni
2025-07-14 11:34:38 +08:00
parent 3b6641c1fb
commit 943ce5b06f
1635 changed files with 603092 additions and 0 deletions
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sensor_msgs/CameraInfo camera_info
geometry_msgs/Transform local_transform
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CameraModel[] models
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std_msgs/Header header
# EnvSensor
int32 type
float64 value
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float64 stamp # in seconds
float64 longitude # DD format
float64 latitude # DD format
float64 altitude # in meters
float64 error # in meters
float64 bearing # North 0->360 deg
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std_msgs/Header header
# compressed global descriptor
# use rtabmap::util3d::uncompressData() from "rtabmap/core/util3d.h"
int32 type
uint8[] info
uint8[] data
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std_msgs/Header header
# Set either node_id or node_label
int32 node_id
string node_label
# optional: if not set, the base frame of the robot is used
string frame_id
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########################################
# RTAB-Map info with statistics
########################################
std_msgs/Header header
int32 ref_id
int32 loop_closure_id
int32 proximity_detection_id
int32 landmark_id
geometry_msgs/Transform loop_closure_transform
####
# For statistics...
####
# State (node IDs) of the current Working Memory (including STM)
int32[] wm_state
# std::map<int, float> posterior;
int32[] posterior_keys
float32[] posterior_values
# std::map<int, float> likelihood;
int32[] likelihood_keys
float32[] likelihood_values
# std::map<int, float> raw_likelihood;
int32[] raw_likelihood_keys
float32[] raw_likelihood_values
# std::map<int, int> weights;
int32[] weights_keys
int32[] weights_values
# std::map<int, std::string> labels;
int32[] labels_keys
string[] labels_values
# std::map<std::string, float> stats
string[] stats_keys
float32[] stats_values
# std::vector<int> local_path
int32[] local_path
int32 current_goal_id
# std::vector<int> odomCache
MapGraph odom_cache
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#class cv::KeyPoint
#{
# Point2f pt;
# float size;
# float angle;
# float response;
# int octave;
# int class_id;
#}
Point2f pt
float32 size
float32 angle
float32 response
int32 octave
int32 class_id
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# header.stamp: the timestamp of the detection (e.g. image timestamp)
# header.frame_id: the base frame of pose (e.g., camera optical frame)
std_msgs/Header header
# Landmark's frame id
string landmark_frame_id
# Landmark's unique ID: should be >0
int32 id
# Size in meters of the landmark/tag (optional, set 0 to not use it).
float32 size
# Pose of the landmark in header.frame_id frame.
# If covariance is unknown, keep it as null matrix.
# rtabmap_slam/rtabmap's landmark_angular_variance and
# landmark_linear_variance parameters can be used
# for convenience if covariance is null.
geometry_msgs/PoseWithCovariance pose
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# header.stamp: the timestamp of the detection (e.g. image timestamp)
# header.frame_id: the base frame of pose (e.g., camera optical frame)
std_msgs/Header header
LandmarkDetection[] landmarks
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#class rtabmap::Link
#{
# int from;
# int to;
# Type type;
# Transform transform;
# cv::Mat(6,6,CV_64FC1) information;
#}
int32 from_id
int32 to_id
int32 type
geometry_msgs/Transform transform
float64[36] information
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std_msgs/Header header
##################
# Optimized graph
##################
MapGraph graph
##################
# Graph data
##################
Node[] nodes
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std_msgs/Header header
##
# /map to /odom transform
# Always identity when the graph is optimized from the latest pose.
##
geometry_msgs/Transform map_to_odom
# The poses
int32[] poses_id
geometry_msgs/Pose[] poses
# The links
Link[] links
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#class rtabmap::Signature
int32 id
int32 map_id
int32 weight
float64 stamp
string label
# Pose from odometry not corrected
geometry_msgs/Pose pose
# std::multimap<wordId, index>
# std::vector<cv::Keypoint>
# std::vector<cv::Point3f>
int32[] word_id_keys
int32[] word_id_values
KeyPoint[] word_kpts
Point3f[] word_pts
# compressed descriptors
# use rtabmap::util3d::uncompressData() from "rtabmap/core/util3d.h"
uint8[] word_descriptors
SensorData data
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std_msgs/Header header
bool lost
int32 matches
int32 inliers
float32 icp_inliers_ratio
float32 icp_rotation
float32 icp_translation
float32 icp_structural_complexity
float32 icp_structural_distribution
int32 icp_correspondences
float64[36] covariance
int32 features
int32 local_map_size
int32 local_scan_map_size
int32 local_key_frames
int32 local_bundle_outliers
int32 local_bundle_constraints
float32 local_bundle_time
float32 local_bundle_avg_inlier_distance
int32 local_bundle_max_key_frames_for_inlier
bool key_frame_added
float32 time_estimation
float32 time_particle_filtering
float32 stamp
float32 interval
float32 distance_travelled
int32 memory_usage # MB
float32 gravity_roll_error
float32 gravity_pitch_error
# Local bundle camera ids
int32[] local_bundle_ids
# Local bundle camera models
CameraModels[] local_bundle_models
# Local bundle camera poses
geometry_msgs/Pose[] local_bundle_poses
geometry_msgs/Transform transform
geometry_msgs/Transform transform_filtered
geometry_msgs/Transform transform_ground_truth
geometry_msgs/Transform guess
# 0=F2M, 1=F2F
int32 type
# F2M odometry
# std::multimap<int, cv::KeyPoint> words;
# std::vector<int> wordMatches;
# std::vector<int> wordInliers;
int32[] words_keys
KeyPoint[] words_values
int32[] word_matches
int32[] word_inliers
int32[] local_map_keys
Point3f[] local_map_values
# local scan map data
sensor_msgs/PointCloud2 local_scan_map
# F2F odometry
# std::vector<cv::Point2f> ref_corners;
# std::vector<cv::Point2f> new_corners;
# std::vector<int> corner_inliers;
Point2f[] ref_corners
Point2f[] new_corners
int32[] corner_inliers
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std_msgs/Header header
int32[] node_ids
geometry_msgs/Pose[] poses
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#class cv::Point2f
#{
# float x;
# float y;
#}
float32 x
float32 y
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#class cv::Point3f
#{
# float x;
# float y;
# float z;
#}
float32 x
float32 y
float32 z
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std_msgs/Header header
# For stereo, rgb corresponds to left camera, and depth the right camera.
# camera info
sensor_msgs/CameraInfo rgb_camera_info
sensor_msgs/CameraInfo depth_camera_info
# Raw
sensor_msgs/Image rgb
sensor_msgs/Image depth
# Compressed
sensor_msgs/CompressedImage rgb_compressed
sensor_msgs/CompressedImage depth_compressed
# Local features
KeyPoint[] key_points
Point3f[] points
# compressed descriptors
# use rtabmap::util3d::uncompressData() from "rtabmap/core/util3d.h"
uint8[] descriptors
GlobalDescriptor global_descriptor
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std_msgs/Header header
RGBDImage[] rgbd_images
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std_msgs/Header header
# scan or scan_cloud is set
sensor_msgs/LaserScan scan
sensor_msgs/PointCloud2 scan_cloud
GlobalDescriptor global_descriptor
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#class rtabmap::SensorData
std_msgs/Header header
# For RGB-D, left corresponds to rgb camera, and right corresponds to depth camera.
# Raw images
sensor_msgs/Image left
sensor_msgs/Image right
# Compressed images
# use rtabmap::util3d::uncompressImage() from "rtabmap/core/util3d.h"
uint8[] left_compressed
uint8[] right_compressed
# Camera info
sensor_msgs/CameraInfo[] left_camera_info
sensor_msgs/CameraInfo[] right_camera_info
# Transform from base frame to camera frame
geometry_msgs/Transform[] local_transform
# raw 2d or 3D laser scan
sensor_msgs/PointCloud2 laser_scan
# compressed 2D or 3D laser scan
# use rtabmap::util3d::uncompressData() from "rtabmap/core/util3d.h"
uint8[] laser_scan_compressed
int32 laser_scan_max_pts
float32 laser_scan_max_range
int32 laser_scan_format
# local transform (base frame -> laser frame)
geometry_msgs/Transform laser_scan_local_transform
# compressed user data
# use rtabmap::util3d::uncompressData() from "rtabmap/core/util3d.h"
uint8[] user_data
# compressed occupancy grid
# use rtabmap::util3d::uncompressData() from "rtabmap/core/util3d.h"
uint8[] grid_ground
uint8[] grid_obstacles
uint8[] grid_empty_cells
float32 grid_cell_size
Point3f grid_view_point
# Local features
KeyPoint[] key_points
Point3f[] points
# compressed descriptors
# use rtabmap::util3d::uncompressData() from "rtabmap/core/util3d.h"
uint8[] descriptors
GlobalDescriptor[] global_descriptors
EnvSensor[] env_sensors
sensor_msgs/Imu imu
geometry_msgs/Transform imu_local_transform
LandmarkDetection[] landmarks
# Ground truth
geometry_msgs/Pose ground_truth_pose
# GPS
GPS gps
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std_msgs/Header header
# OpenCV matrix containing the user data. A matrix of type CV_8UC1
# with 1 row is considered to be compressed (with rtabmap::compressData() method).
# If you have one dimension unsigned 8 bits uncompressed data, make sure to transpose it
# (to have multiple rows instead of multiple columns) in order to be detected as
# not compressed.
uint32 rows
uint32 cols
uint32 type
uint8[] data