489 lines
16 KiB
C++
489 lines
16 KiB
C++
/*********************************************************************
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*
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* Software License Agreement (BSD License)
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*
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* Copyright (c) 2008, 2013, Willow Garage, Inc.
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of Willow Garage, Inc. nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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* Author: Eitan Marder-Eppstein
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* David V. Lu!!
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*********************************************************************/
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#include "nav2_costmap_2d/inflation_layer.hpp"
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#include <limits>
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#include <map>
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#include <vector>
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#include <algorithm>
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#include <utility>
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#include "nav2_costmap_2d/costmap_math.hpp"
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#include "nav2_costmap_2d/footprint.hpp"
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#include "pluginlib/class_list_macros.hpp"
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#include "rclcpp/parameter_events_filter.hpp"
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PLUGINLIB_EXPORT_CLASS(nav2_costmap_2d::InflationLayer, nav2_costmap_2d::Layer)
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using nav2_costmap_2d::LETHAL_OBSTACLE;
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using nav2_costmap_2d::INSCRIBED_INFLATED_OBSTACLE;
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using nav2_costmap_2d::NO_INFORMATION;
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using rcl_interfaces::msg::ParameterType;
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namespace nav2_costmap_2d
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{
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InflationLayer::InflationLayer()
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: inflation_radius_(0),
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inscribed_radius_(0),
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cost_scaling_factor_(0),
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inflate_unknown_(false),
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inflate_around_unknown_(false),
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cell_inflation_radius_(0),
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cached_cell_inflation_radius_(0),
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resolution_(0),
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cache_length_(0),
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last_min_x_(std::numeric_limits<double>::lowest()),
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last_min_y_(std::numeric_limits<double>::lowest()),
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last_max_x_(std::numeric_limits<double>::max()),
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last_max_y_(std::numeric_limits<double>::max())
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{
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access_ = new mutex_t();
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}
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InflationLayer::~InflationLayer()
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{
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dyn_params_handler_.reset();
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delete access_;
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}
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void
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InflationLayer::onInitialize()
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{
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declareParameter("enabled", rclcpp::ParameterValue(true));
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declareParameter("inflation_radius", rclcpp::ParameterValue(0.55));
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declareParameter("cost_scaling_factor", rclcpp::ParameterValue(10.0));
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declareParameter("inflate_unknown", rclcpp::ParameterValue(false));
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declareParameter("inflate_around_unknown", rclcpp::ParameterValue(false));
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{
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auto node = node_.lock();
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if (!node) {
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throw std::runtime_error{"Failed to lock node"};
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}
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node->get_parameter(name_ + "." + "enabled", enabled_);
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node->get_parameter(name_ + "." + "inflation_radius", inflation_radius_);
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node->get_parameter(name_ + "." + "cost_scaling_factor", cost_scaling_factor_);
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node->get_parameter(name_ + "." + "inflate_unknown", inflate_unknown_);
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node->get_parameter(name_ + "." + "inflate_around_unknown", inflate_around_unknown_);
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dyn_params_handler_ = node->add_on_set_parameters_callback(
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std::bind(
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&InflationLayer::dynamicParametersCallback,
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this, std::placeholders::_1));
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}
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current_ = true;
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seen_.clear();
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cached_distances_.clear();
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cached_costs_.clear();
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need_reinflation_ = false;
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cell_inflation_radius_ = cellDistance(inflation_radius_);
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matchSize();
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}
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void
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InflationLayer::matchSize()
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{
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std::lock_guard<Costmap2D::mutex_t> guard(*getMutex());
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nav2_costmap_2d::Costmap2D * costmap = layered_costmap_->getCostmap();
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resolution_ = costmap->getResolution();
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cell_inflation_radius_ = cellDistance(inflation_radius_);
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computeCaches();
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seen_ = std::vector<bool>(costmap->getSizeInCellsX() * costmap->getSizeInCellsY(), false);
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}
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void
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InflationLayer::updateBounds(
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double /*robot_x*/, double /*robot_y*/, double /*robot_yaw*/, double * min_x,
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double * min_y, double * max_x, double * max_y)
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{
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std::lock_guard<Costmap2D::mutex_t> guard(*getMutex());
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if (need_reinflation_) {
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last_min_x_ = *min_x;
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last_min_y_ = *min_y;
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last_max_x_ = *max_x;
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last_max_y_ = *max_y;
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*min_x = std::numeric_limits<double>::lowest();
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*min_y = std::numeric_limits<double>::lowest();
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*max_x = std::numeric_limits<double>::max();
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*max_y = std::numeric_limits<double>::max();
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need_reinflation_ = false;
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} else {
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double tmp_min_x = last_min_x_;
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double tmp_min_y = last_min_y_;
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double tmp_max_x = last_max_x_;
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double tmp_max_y = last_max_y_;
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last_min_x_ = *min_x;
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last_min_y_ = *min_y;
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last_max_x_ = *max_x;
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last_max_y_ = *max_y;
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*min_x = std::min(tmp_min_x, *min_x) - inflation_radius_;
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*min_y = std::min(tmp_min_y, *min_y) - inflation_radius_;
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*max_x = std::max(tmp_max_x, *max_x) + inflation_radius_;
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*max_y = std::max(tmp_max_y, *max_y) + inflation_radius_;
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}
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}
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void
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InflationLayer::onFootprintChanged()
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{
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std::lock_guard<Costmap2D::mutex_t> guard(*getMutex());
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inscribed_radius_ = layered_costmap_->getInscribedRadius();
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cell_inflation_radius_ = cellDistance(inflation_radius_);
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computeCaches();
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need_reinflation_ = true;
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if (inflation_radius_ < inscribed_radius_) {
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RCLCPP_ERROR(
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logger_,
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"The configured inflation radius (%.3f) is smaller than "
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"the computed inscribed radius (%.3f) of your footprint, "
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"it is highly recommended to set inflation radius to be at "
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"least as big as the inscribed radius to avoid collisions",
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inflation_radius_, inscribed_radius_);
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}
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RCLCPP_DEBUG(
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logger_, "InflationLayer::onFootprintChanged(): num footprint points: %zu,"
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" inscribed_radius_ = %.3f, inflation_radius_ = %.3f",
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layered_costmap_->getFootprint().size(), inscribed_radius_, inflation_radius_);
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}
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void
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InflationLayer::updateCosts(
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nav2_costmap_2d::Costmap2D & master_grid, int min_i, int min_j,
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int max_i,
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int max_j)
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{
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std::lock_guard<Costmap2D::mutex_t> guard(*getMutex());
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if (!enabled_ || (cell_inflation_radius_ == 0)) {
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return;
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}
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// make sure the inflation list is empty at the beginning of the cycle (should always be true)
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for (auto & dist : inflation_cells_) {
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RCLCPP_FATAL_EXPRESSION(
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logger_,
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!dist.empty(), "The inflation list must be empty at the beginning of inflation");
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}
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unsigned char * master_array = master_grid.getCharMap();
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unsigned int size_x = master_grid.getSizeInCellsX(), size_y = master_grid.getSizeInCellsY();
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if (seen_.size() != size_x * size_y) {
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RCLCPP_WARN(
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logger_, "InflationLayer::updateCosts(): seen_ vector size is wrong");
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seen_ = std::vector<bool>(size_x * size_y, false);
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}
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std::fill(begin(seen_), end(seen_), false);
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// We need to include in the inflation cells outside the bounding
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// box min_i...max_j, by the amount cell_inflation_radius_. Cells
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// up to that distance outside the box can still influence the costs
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// stored in cells inside the box.
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const int base_min_i = min_i;
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const int base_min_j = min_j;
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const int base_max_i = max_i;
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const int base_max_j = max_j;
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min_i -= static_cast<int>(cell_inflation_radius_);
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min_j -= static_cast<int>(cell_inflation_radius_);
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max_i += static_cast<int>(cell_inflation_radius_);
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max_j += static_cast<int>(cell_inflation_radius_);
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min_i = std::max(0, min_i);
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min_j = std::max(0, min_j);
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max_i = std::min(static_cast<int>(size_x), max_i);
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max_j = std::min(static_cast<int>(size_y), max_j);
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// Inflation list; we append cells to visit in a list associated with
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// its distance to the nearest obstacle
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// We use a map<distance, list> to emulate the priority queue used before,
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// with a notable performance boost
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// Start with lethal obstacles: by definition distance is 0.0
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auto & obs_bin = inflation_cells_[0];
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for (int j = min_j; j < max_j; j++) {
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for (int i = min_i; i < max_i; i++) {
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int index = static_cast<int>(master_grid.getIndex(i, j));
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unsigned char cost = master_array[index];
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if (cost == LETHAL_OBSTACLE || (inflate_around_unknown_ && cost == NO_INFORMATION)) {
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obs_bin.emplace_back(index, i, j, i, j);
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}
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}
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}
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// Process cells by increasing distance; new cells are appended to the
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// corresponding distance bin, so they
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// can overtake previously inserted but farther away cells
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for (const auto & dist_bin : inflation_cells_) {
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for (std::size_t i = 0; i < dist_bin.size(); ++i) {
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// Do not use iterator or for-range based loops to
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// iterate though dist_bin, since it's size might
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// change when a new cell is enqueued, invalidating all iterators
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unsigned int index = dist_bin[i].index_;
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// ignore if already visited
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if (seen_[index]) {
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continue;
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}
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seen_[index] = true;
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unsigned int mx = dist_bin[i].x_;
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unsigned int my = dist_bin[i].y_;
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unsigned int sx = dist_bin[i].src_x_;
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unsigned int sy = dist_bin[i].src_y_;
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// assign the cost associated with the distance from an obstacle to the cell
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unsigned char cost = costLookup(mx, my, sx, sy);
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unsigned char old_cost = master_array[index];
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// In order to avoid artifacts appeared out of boundary areas
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// when some layer is going after inflation_layer,
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// we need to apply inflation_layer only to inside of given bounds
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if (static_cast<int>(mx) >= base_min_i &&
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static_cast<int>(my) >= base_min_j &&
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static_cast<int>(mx) < base_max_i &&
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static_cast<int>(my) < base_max_j)
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{
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if (old_cost == NO_INFORMATION &&
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(inflate_unknown_ ? (cost > FREE_SPACE) : (cost >= INSCRIBED_INFLATED_OBSTACLE)))
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{
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master_array[index] = cost;
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} else {
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master_array[index] = std::max(old_cost, cost);
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}
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}
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// attempt to put the neighbors of the current cell onto the inflation list
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if (mx > 0) {
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enqueue(index - 1, mx - 1, my, sx, sy);
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}
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if (my > 0) {
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enqueue(index - size_x, mx, my - 1, sx, sy);
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}
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if (mx < size_x - 1) {
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enqueue(index + 1, mx + 1, my, sx, sy);
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}
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if (my < size_y - 1) {
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enqueue(index + size_x, mx, my + 1, sx, sy);
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}
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}
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}
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for (auto & dist : inflation_cells_) {
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dist.clear();
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dist.reserve(200);
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}
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current_ = true;
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}
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/**
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* @brief Given an index of a cell in the costmap, place it into a list pending for obstacle inflation
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* @param grid The costmap
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* @param index The index of the cell
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* @param mx The x coordinate of the cell (can be computed from the index, but saves time to store it)
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* @param my The y coordinate of the cell (can be computed from the index, but saves time to store it)
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* @param src_x The x index of the obstacle point inflation started at
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* @param src_y The y index of the obstacle point inflation started at
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*/
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void
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InflationLayer::enqueue(
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unsigned int index, unsigned int mx, unsigned int my,
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unsigned int src_x, unsigned int src_y)
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{
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if (!seen_[index]) {
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// we compute our distance table one cell further than the
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// inflation radius dictates so we can make the check below
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double distance = distanceLookup(mx, my, src_x, src_y);
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// we only want to put the cell in the list if it is within
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// the inflation radius of the obstacle point
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if (distance > cell_inflation_radius_) {
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return;
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}
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const unsigned int r = cell_inflation_radius_ + 2;
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// push the cell data onto the inflation list and mark
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inflation_cells_[distance_matrix_[mx - src_x + r][my - src_y + r]].emplace_back(
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index, mx, my, src_x, src_y);
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}
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}
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void
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InflationLayer::computeCaches()
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{
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std::lock_guard<Costmap2D::mutex_t> guard(*getMutex());
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if (cell_inflation_radius_ == 0) {
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return;
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}
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cache_length_ = cell_inflation_radius_ + 2;
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// based on the inflation radius... compute distance and cost caches
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if (cell_inflation_radius_ != cached_cell_inflation_radius_) {
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cached_costs_.resize(cache_length_ * cache_length_);
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cached_distances_.resize(cache_length_ * cache_length_);
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for (unsigned int i = 0; i < cache_length_; ++i) {
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for (unsigned int j = 0; j < cache_length_; ++j) {
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cached_distances_[i * cache_length_ + j] = hypot(i, j);
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}
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}
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cached_cell_inflation_radius_ = cell_inflation_radius_;
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}
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for (unsigned int i = 0; i < cache_length_; ++i) {
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for (unsigned int j = 0; j < cache_length_; ++j) {
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cached_costs_[i * cache_length_ + j] = computeCost(cached_distances_[i * cache_length_ + j]);
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}
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}
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int max_dist = generateIntegerDistances();
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inflation_cells_.clear();
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inflation_cells_.resize(max_dist + 1);
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for (auto & dist : inflation_cells_) {
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dist.reserve(200);
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}
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}
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int
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InflationLayer::generateIntegerDistances()
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{
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const int r = cell_inflation_radius_ + 2;
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const int size = r * 2 + 1;
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std::vector<std::pair<int, int>> points;
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for (int y = -r; y <= r; y++) {
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for (int x = -r; x <= r; x++) {
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if (x * x + y * y <= r * r) {
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points.emplace_back(x, y);
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}
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}
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}
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std::sort(
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points.begin(), points.end(),
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[](const std::pair<int, int> & a, const std::pair<int, int> & b) -> bool {
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return a.first * a.first + a.second * a.second < b.first * b.first + b.second * b.second;
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}
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);
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std::vector<std::vector<int>> distance_matrix(size, std::vector<int>(size, 0));
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std::pair<int, int> last = {0, 0};
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int level = 0;
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for (auto const & p : points) {
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if (p.first * p.first + p.second * p.second !=
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last.first * last.first + last.second * last.second)
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{
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level++;
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}
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distance_matrix[p.first + r][p.second + r] = level;
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last = p;
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}
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distance_matrix_ = distance_matrix;
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return level;
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}
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/**
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* @brief Callback executed when a parameter change is detected
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* @param event ParameterEvent message
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*/
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rcl_interfaces::msg::SetParametersResult
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InflationLayer::dynamicParametersCallback(
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std::vector<rclcpp::Parameter> parameters)
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{
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std::lock_guard<Costmap2D::mutex_t> guard(*getMutex());
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rcl_interfaces::msg::SetParametersResult result;
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bool need_cache_recompute = false;
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for (auto parameter : parameters) {
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const auto & param_type = parameter.get_type();
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const auto & param_name = parameter.get_name();
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if (param_type == ParameterType::PARAMETER_DOUBLE) {
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if (param_name == name_ + "." + "inflation_radius" &&
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inflation_radius_ != parameter.as_double())
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{
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inflation_radius_ = parameter.as_double();
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need_reinflation_ = true;
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need_cache_recompute = true;
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} else if (param_name == name_ + "." + "cost_scaling_factor" && // NOLINT
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cost_scaling_factor_ != parameter.as_double())
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{
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cost_scaling_factor_ = parameter.as_double();
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need_reinflation_ = true;
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need_cache_recompute = true;
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}
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} else if (param_type == ParameterType::PARAMETER_BOOL) {
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if (param_name == name_ + "." + "enabled" && enabled_ != parameter.as_bool()) {
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enabled_ = parameter.as_bool();
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need_reinflation_ = true;
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current_ = false;
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} else if (param_name == name_ + "." + "inflate_unknown" && // NOLINT
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inflate_unknown_ != parameter.as_bool())
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{
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inflate_unknown_ = parameter.as_bool();
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need_reinflation_ = true;
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} else if (param_name == name_ + "." + "inflate_around_unknown" && // NOLINT
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|
inflate_around_unknown_ != parameter.as_bool())
|
|
{
|
|
inflate_around_unknown_ = parameter.as_bool();
|
|
need_reinflation_ = true;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (need_cache_recompute) {
|
|
matchSize();
|
|
}
|
|
|
|
result.successful = true;
|
|
return result;
|
|
}
|
|
|
|
} // namespace nav2_costmap_2d
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