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ros2 学习launch文件组织工程 yaml配置文件_ros2工程管理
作者：很楠不爱3 | 2024-05-11 16:15:11
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ros2工程管理
简单范例
功能描述
使用launch文件，统一管理工程，实现img转点云，发送到img_pt的topic，然后用reg_pcl节点进行subscribe，进行点云配准处理，输出融合后的点云到map_pt的topic。最后由rviz2进行点云展示。
环境准备
ubuntu18.04+ros2（eloquent此版本需要于Ubuntu对应）+ Python（version=3.6需要于eloquent对应）+PCL+EIGEN+rviz2
具体安装库和依赖不做赘述
代码实现
代码结构
代码结构编译前
pcl_reg ├── CMakeLists.txt ├── config │ ├── img2pt.yaml │ └── reg_pcl.yaml ├── include │ └── 空 ├── launch │ ├── img2pt.launch.py │ ├── mapping.launch.py │ ├── mapping_sub.launch.py │ ├── reg_pcl.launch.py │ └── showviz.launch.py ├── package.xml ├── rviz │ └── 空 └── src ├── img2pt.cpp ├── odom_pre.cpp ├── other.txt ├── pt_show.cpp └── reg_pcl.cpp
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代码内容
img转pt的部分，图像为rgb三通道的语义图，转换为点云，点云为pcl::PointXYZRGB的格式，XYZ为点云位置，RGB对应图像的三通道rgb。其中XY可以通过图像的u，v坐标转换得到，因为是BEV视角，Z坐标统一置零即可。然后由于语义图上的点云数量过多，在后续匹配的过程中中会降低效率。可以做一些预处理，例如下采样，设置权值等。
map2pt.cpp
#include <iostream> #include <chrono> #include <memory> #include <rclcpp/rclcpp.hpp> #include <sensor_msgs/msg/point_cloud2.hpp> #include "pcl_conversions/pcl_conversions.h"//for pcl::toROSMsg #include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/point_cloud.h> #include <pcl/common/common.h> #include <pcl/common/transforms.h> #include <pcl/filters/voxel_grid.h> #include <pcl/kdtree/kdtree_flann.h> #include <opencv2/opencv.hpp> #include <Eigen/Dense> // typedef pcl::PointXYZRGB pcl::PointXYZRGB; using namespace std::chrono_literals; class PointCloudPublisher : public rclcpp::Node { public: PointCloudPublisher() : Node("point_cloud_publisher") { count = 1380; param_respond();//参数初始化 // timer_param_ = this->create_wall_timer(500ms, std::bind(&PointCloudPublisher::param_respond, this)); // 创建一个Publisher，发布PointCloud2消息到名为"pt"的topic publisher_ = this->create_publisher<sensor_msgs::msg::PointCloud2>("img_pt", 10); // 创建一个定时器，每秒钟发布一次PointCloud2消息 std::chrono::seconds(1) timer_ = this->create_wall_timer(1s, std::bind(&PointCloudPublisher::publishPointCloud, this)); } private: //使用yaml进行参数配置，此处参数只会在初始化时生效一次。如果需要参数随时变化，需要做其他处理 void param_respond() { //定义可配置参数，与yaml文件或launch文件中参数名需要对应，需要注意的是参数类型需要匹配，否则会报错。并给出参数默认值。 this->declare_parameter<float>("voxel_grid_x", 0.05); this->declare_parameter<float>("voxel_grid_y", 0.05); this->declare_parameter<float>("voxel_grid_z", 0.05); //获取参数,并将参数值赋值到对应变量 this->get_parameter("voxel_grid_x", voxel_grid_x_); this->get_parameter("voxel_grid_y", voxel_grid_y_); this->get_parameter("voxel_grid_z", voxel_grid_z_); //打印 RCLCPP_INFO(this->get_logger(), "Hello voxel_grid_x:%f, voxel_grid_y:%f, voxel_grid_z:%f", voxel_grid_x_,voxel_grid_y_,voxel_grid_z_); //将参数统一放到std::vector<rclcpp::Parameter>中并set到节点里，此时参数文件yaml或launch文件中的数值开始生效。 std::vector<rclcpp::Parameter> all_new_parameters{rclcpp::Parameter("voxel_grid_x", voxel_grid_x_),rclcpp::Parameter("voxel_grid_y", voxel_grid_y_),rclcpp::Parameter("voxel_grid_z", voxel_grid_z_)}; this->set_parameters(all_new_parameters); } void publishPointCloud() { // 创建一个PointCloud对象 pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_src(new pcl::PointCloud<pcl::PointXYZRGB>); std::string path_img = "/home/apollo/zr/code/data/sim_data/camera1-230829-164731_Lanes/"+std::to_string(count)+".jpg"; cv::Mat image = cv::imread(path_img); // 检查图片是否成功加载 if (image.empty()) { std::cout << "无法加载图片" << std::endl; // return -1; } RCLCPP_INFO(this->get_logger(), "Publishing: '%d'", count); count=count+12; pcl::PointCloud<pcl::PointXYZRGB>::Ptr pointCloud(new pcl::PointCloud<pcl::PointXYZRGB>); Eigen::Matrix3f matK_ipm_inverse = Eigen::Matrix3f::Identity(3, 3); //bias表示车后轴中心在图像像素位置 //k_cam表示图像像素和实际尺度m的比例关系 float k_cam = 0.0325; float x_bias = 480*4.0/5.0*k_cam; float y_bias = 640/2.0*k_cam; matK_ipm_inverse << 0,-k_cam,x_bias, -k_cam,0,y_bias, 0,0,1; pointCloud = ipmToPointCloud(image,matK_ipm_inverse); // /* // 点云降采样 // create new keyFrame, and add pointCloud to global map bool make_global_map = true; pcl::PointCloud<pcl::PointXYZRGB>::Ptr downsampled_cloud(new pcl::PointCloud<pcl::PointXYZRGB>); if(make_global_map){ // 创建 VoxelGrid 对象 pcl::VoxelGrid<pcl::PointXYZRGB> voxel_grid; voxel_grid.setInputCloud(pointCloud); voxel_grid.setLeafSize(voxel_grid_x_, voxel_grid_y_, voxel_grid_z_); // 设置体素大小 // 执行下采样 voxel_grid.filter(*downsampled_cloud); // *globalFeatureCloud=globalMapDS; RCLCPP_INFO(this->get_logger(), "globalFeatureCloud size is: '%d'", (int)downsampled_cloud->size()); } // */ // 创建一个PointCloud2消息 sensor_msgs::msg::PointCloud2::UniquePtr cloud_msg(new sensor_msgs::msg::PointCloud2); pcl::toROSMsg(*downsampled_cloud, *cloud_msg); // pcl::toROSMsg(*pointCloud, *cloud_msg); cloud_msg->header.frame_id = "base_link"; // 设置坐标系 cloud_msg->header.stamp = this->now();// 设置PointCloud2消息的时间戳 // 发布PointCloud2消息到"pt"的topic publisher_->publish(std::move(cloud_msg)); } pcl::PointCloud<pcl::PointXYZRGB>::Ptr ipmToPointCloud(const cv::Mat& ipmImage, const Eigen::Matrix3f matK_ipm_inv) { pcl::PointCloud<pcl::PointXYZRGB>::Ptr pointCloudOut(new pcl::PointCloud<pcl::PointXYZRGB>); for (int y = 0; y < ipmImage.rows; ++y) { for (int x = 0; x < ipmImage.cols; ++x) { cv::Vec3b pixel = ipmImage.at<cv::Vec3b>(y, x); if (!(pixel[0]==0 && pixel[1]==0 && pixel[2]==0)) { pcl::PointXYZRGB pclPoint; Eigen::Vector3f uv1(x,y,1); Eigen::Vector3f xyz=matK_ipm_inv*uv1; pclPoint.x = xyz(0); pclPoint.y = xyz(1); pclPoint.z = 0; pclPoint.r = pixel[0]; pclPoint.g = pixel[1]; pclPoint.b = pixel[2]; pointCloudOut->push_back(pclPoint); // if(pixel[0]==233, pixel[1]==238, pixel[2]==12) // { // std::cout<<"u,v:["<<x<<","<<y<<"] x,y:["<<pclPoint.x<<","<<pclPoint.y<<"]"<<std::endl; // } } } } return pointCloudOut; } int count; rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr publisher_; rclcpp::TimerBase::SharedPtr timer_; float voxel_grid_x_; float voxel_grid_y_; float voxel_grid_z_; // rclcpp::TimerBase::SharedPtr timer_param_; }; int main(int argc, char** argv) { rclcpp::init(argc, argv); auto node = std::make_shared<PointCloudPublisher>(); rclcpp::spin(node); rclcpp::shutdown(); /*打开图片: 单独实线功能，不使用ros2系统 */ // cv::Mat image = cv::imread("/home/apollo/zr/code/data/sim_data/camera1-230829-164731_Lanes/1440.jpg"); // // 检查图片是否成功加载 // if (image.empty()) { // std::cout << "无法加载图片" << std::endl; // return -1; // } // pcl::PointCloud<pcl::PointXYZRGB>::Ptr pointCloud(new pcl::PointCloud<pcl::PointXYZRGB>); // Eigen::Matrix3f matK_ipm = Eigen::Matrix3f::Identity(3, 3); // matK_ipm << 0,40,1.798070000000000057e+00, // -40,0,0, // 0,0,1; // pointCloud = ipmToPointCloud(image,matK_ipm); // // 创建窗口并显示图片 // cv::namedWindow("Image", cv::WINDOW_NORMAL); // cv::imshow("Image", image); // // 等待按键退出 // cv::waitKey(0); return 0; }
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reg_pcl.cpp
#include <rclcpp/rclcpp.hpp> #include <sensor_msgs/msg/point_cloud2.hpp> #include <geometry_msgs/msg/transform_stamped.hpp> #include <pcl/point_cloud.h> #include <pcl/point_types.h> #include <pcl_conversions/pcl_conversions.h> #include <iostream> #include <pcl/registration/icp.h> #include <pcl/registration/ndt.h> #include <pcl/common/common.h> #include <pcl/common/transforms.h> #include <pcl/filters/voxel_grid.h> #include <pcl/kdtree/kdtree_flann.h> // 处理点云的函数 class PointCloudProcessor : public rclcpp::Node { public: PointCloudProcessor() : Node("point_cloud_processor") { param_respond();//参数初始化 init_flag = true;//第一帧标志位，第一帧无法进行点云配准 count = 0;//匹配次数，为了保存结果点云计数，可选用 map_RT = Eigen::Matrix4f::Identity();//建图功能累计的位姿矩阵 cloud_local_map = pcl::PointCloud<pcl::PointXYZRGB>::Ptr(new pcl::PointCloud<pcl::PointXYZRGB>); // 创建订阅者，订阅 'img_pt' 话题的点云消息 subscription_ = this->create_subscription<sensor_msgs::msg::PointCloud2>( "img_pt", rclcpp::QoS(10), std::bind(&PointCloudProcessor::pointCloudCallback, this, std::placeholders::_1)); // 创建发布者，发布处理后的点云到 'map_pt' 话题 publisher_ = this->create_publisher<sensor_msgs::msg::PointCloud2>("map_pt", rclcpp::QoS(10)); } private: //使用yaml进行参数配置，此处参数只会在初始化时生效一次。如果需要参数随时变化，需要做其他处理 void param_respond() { //定义可配置参数，与yaml文件或launch文件中参数名需要对应，需要注意的是参数类型需要匹配，否则会报错。并给出参数默认值。 this->declare_parameter<bool>("use_icp", true); this->declare_parameter<int>("icpMaximumIterations", 1); this->declare_parameter<double>("icpMaxCorrespondenceDistance", 1); this->declare_parameter<double>("icpTransformationEpsilon", 1e-10); this->declare_parameter<double>("icpEuclideanFitnessEpsilon", 0.001); this->declare_parameter<double>("icpFitnessScoreThresh", 0.3); this->declare_parameter<double>("ndtTransformationEpsilon", 1e-10); this->declare_parameter<double>("ndtResolution", 0.1); this->declare_parameter<double>("ndtFitnessScoreThresh", 0.3); //获取参数,并将参数值赋值到对应变量 this->get_parameter("use_icp", use_icp_); this->get_parameter("icpMaximumIterations", icpMaximumIterations_); this->get_parameter("icpMaxCorrespondenceDistance", icpMaxCorrespondenceDistance_); this->get_parameter("icpTransformationEpsilon", icpTransformationEpsilon_); this->get_parameter("icpEuclideanFitnessEpsilon", icpEuclideanFitnessEpsilon_); this->get_parameter("icpFitnessScoreThresh", icpFitnessScoreThresh_); this->get_parameter("ndtTransformationEpsilon", ndtTransformationEpsilon_); this->get_parameter("ndtResolution", ndtResolution_); this->get_parameter("ndtFitnessScoreThresh", ndtFitnessScoreThresh_); //调试过程中，如果怀疑参数文件有问题，可以使用下方直接赋值方法，确认其他部分没有问题，定位问题到参数文件yaml或launch文件上 // icpTransformationEpsilon_ = 1e-10; // ndtTransformationEpsilon_ = 1e-10; RCLCPP_INFO(this->get_logger(), "Hello use_icp:%d, icpMaximumIterations:%d, icpMaxCorrespondenceDistance:%f", use_icp_,icpMaximumIterations_,icpMaxCorrespondenceDistance_); 将参数统一放到std::vector<rclcpp::Parameter>中并set到节点里，此时参数文件yaml或launch文件中的数值开始生效。 std::vector<rclcpp::Parameter> all_new_parameters{rclcpp::Parameter("use_icp", use_icp_), rclcpp::Parameter("icpMaximumIterations", icpMaximumIterations_), rclcpp::Parameter("icpMaxCorrespondenceDistance", icpMaxCorrespondenceDistance_), rclcpp::Parameter("icpTransformationEpsilon", icpTransformationEpsilon_), rclcpp::Parameter("icpEuclideanFitnessEpsilon", icpEuclideanFitnessEpsilon_), rclcpp::Parameter("icpFitnessScoreThresh", icpFitnessScoreThresh_), rclcpp::Parameter("ndtTransformationEpsilon", ndtTransformationEpsilon_), rclcpp::Parameter("ndtResolution", ndtResolution_), rclcpp::Parameter("ndtFitnessScoreThresh", ndtFitnessScoreThresh_)}; this->set_parameters(all_new_parameters); } void pointCloudCallback(const sensor_msgs::msg::PointCloud2::SharedPtr input_msg) { std::cout<<"rcv pt msg"<<std::endl; // 将接收到的点云消息转换为 pcl::PointCloud<pcl::PointXYZRGB> 类型 pcl::PointCloud<pcl::PointXYZRGB>::Ptr cameraCloudIn(new pcl::PointCloud<pcl::PointXYZRGB>); pcl::fromROSMsg(*input_msg, *cameraCloudIn); if(init_flag) { //第一帧不做匹配，仅保存 cloud_src = cameraCloudIn; cloud_local_map = cameraCloudIn; init_flag = false; return ; } else { cloud_tgt = cloud_src; cloud_src = cameraCloudIn; //计时开始 rclcpp::Clock steady_clock_{RCL_STEADY_TIME}; auto start_time = steady_clock_.now(); // 点云配准 mapping_local(cloud_src,cloud_tgt); // 计时结束并计算cost auto cycle_duration = steady_clock_.now() - start_time; std::cout<<"icp cost time:"<<cycle_duration.seconds()<<" s"<<std::endl; RCLCPP_INFO(get_logger(), "Cost %.4f s", cycle_duration.seconds()); } // 将处理后的点云转换为 sensor_msgs::msg::PointCloud2 类型 sensor_msgs::msg::PointCloud2::UniquePtr result_msg(new sensor_msgs::msg::PointCloud2); pcl::toROSMsg(*cloud_local_map, *result_msg); // pcl::toROSMsg(*cloud_src, *result_msg); result_msg->header.frame_id = "base_link2"; // 设置坐标系 result_msg->header.stamp = this->now();// 设置PointCloud2消息的时间戳 // 发布处理后的点云到 'map_pt' 话题 publisher_->publish(std::move(result_msg)); } void mapping_local(const pcl::PointCloud<pcl::PointXYZRGB>::Ptr& src_cloud,const pcl::PointCloud<pcl::PointXYZRGB>::Ptr& tgt_cloud) { Eigen::Matrix4f transWorldCurrent = Eigen::Matrix4f::Identity(); transWorldCurrent(0,3)=1; // printMat4(transWorldCurrent); pcl::PointCloud<pcl::PointXYZRGB>::Ptr currentFeatureCloudInWorld(new pcl::PointCloud<pcl::PointXYZRGB>()); if(use_icp_) { static pcl::IterativeClosestPoint<pcl::PointXYZRGB, pcl::PointXYZRGB> icp; icp.setMaxCorrespondenceDistance(icpMaxCorrespondenceDistance_); icp.setMaximumIterations(icpMaximumIterations_); icp.setTransformationEpsilon(icpTransformationEpsilon_); icp.setEuclideanFitnessEpsilon(icpEuclideanFitnessEpsilon_); icp.setInputSource(src_cloud); icp.setInputTarget(tgt_cloud); pcl::PointCloud<pcl::PointXYZRGB>::Ptr transCurrentCloudInWorld(new pcl::PointCloud<pcl::PointXYZRGB>()); icp.align(*transCurrentCloudInWorld,transWorldCurrent.matrix());//迭代效果不理想，原因尚未定位到？？？ if (icp.hasConverged() == false || icp.getFitnessScore() > icpFitnessScoreThresh_) { std::cout << "ICP locolization failed the score is " << icp.getFitnessScore() << std::endl; return ; } else { transWorldCurrent = icp.getFinalTransformation(); printMat4(transWorldCurrent); } } else { pcl::NormalDistributionsTransform<pcl::PointXYZRGB, pcl::PointXYZRGB> ndt; ndt.setTransformationEpsilon(ndtTransformationEpsilon_); ndt.setResolution(ndtResolution_); ndt.setInputSource(src_cloud); ndt.setInputTarget(tgt_cloud); pcl::PointCloud<pcl::PointXYZRGB>::Ptr transCurrentCloudInWorld(new pcl::PointCloud<pcl::PointXYZRGB>()); ndt.align(*transCurrentCloudInWorld, transWorldCurrent.matrix());//会直接退出程序，原因尚未定位到？？？ if (ndt.hasConverged() == false || ndt.getFitnessScore() > ndtFitnessScoreThresh_) { std::cout << "ndt locolization failed the score is " << ndt.getFitnessScore() << std::endl; return ; } else { transWorldCurrent =ndt.getFinalTransformation(); printMat4(transWorldCurrent); } } map_RT = map_RT*transWorldCurrent; pcl::transformPointCloud (*src_cloud, *currentFeatureCloudInWorld, map_RT); /* // 保存pcd文件，方便pcl_viewer <pcdfile.pcd> count++; std::string filename1 = "/home/apollo/zr/code/resultpcd/src_tgt"+std::to_string(count)+".pcd"; pcl::PointCloud<pcl::PointXYZRGB>::Ptr show_cloud1(new pcl::PointCloud<pcl::PointXYZRGB>); *show_cloud1 = *show_cloud1+*src_cloud; *show_cloud1 = *show_cloud1+*tgt_cloud; pcl::io::savePCDFileBinary(filename1, *show_cloud1); std::string filename2 = "/home/apollo/zr/code/resultpcd/tgt_trans"+std::to_string(count)+".pcd"; pcl::PointCloud<pcl::PointXYZRGB>::Ptr show_cloud2(new pcl::PointCloud<pcl::PointXYZRGB>); *show_cloud2 = *show_cloud2+*tgt_cloud; *show_cloud2 = *show_cloud2+*currentFeatureCloudInWorld; pcl::io::savePCDFileBinary(filename2, *show_cloud2); */ // 创建新的帧，并将src点云（后续需要换成匹配点云点对）添加进map点云 *cloud_local_map = *cloud_local_map+*currentFeatureCloudInWorld; RCLCPP_INFO(this->get_logger(), "cloud_local_map size is: '%d'", (int)cloud_local_map->size()); // } rclcpp::Subscription<sensor_msgs::msg::PointCloud2>::SharedPtr subscription_; rclcpp::Subscription<geometry_msgs::msg::TransformStamped>::SharedPtr subscription_odom_; rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr publisher_; Eigen::Matrix4f map_RT; pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_local_map; pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_src; //不需要初始化，后面会赋值 pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_tgt;//不需要初始化，后面会赋值 bool init_flag; int count; bool use_icp_ = true; double icpMaxCorrespondenceDistance_=1;//100 int icpMaximumIterations_=1;//100 double icpTransformationEpsilon_=1e-10; double icpEuclideanFitnessEpsilon_=0.001; double icpFitnessScoreThresh_=0.3;//0.3 double ndtTransformationEpsilon_=1e-10; double ndtResolution_=0.1; double ndtFitnessScoreThresh_=0.3; void printMat4(Eigen::Matrix4f mat) { std::cout<<"matrix is :"<<std::endl; for(int i=0;i<mat.rows();i++) { for(int j=0;j<mat.cols();j++) { std::cout<<mat(i,j)<<" , "; } std::cout<<std::endl; } } }; int main(int argc, char** argv) { rclcpp::init(argc, argv); rclcpp::spin(std::make_shared<PointCloudProcessor>()); rclcpp::shutdown(); return 0; }
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对CMakeLists.txt进行适当修改
cmake_minimum_required(VERSION 3.5) project(pcl_reg) # Default to C99 if(NOT CMAKE_C_STANDARD) set(CMAKE_C_STANDARD 99) endif() # Default to C++14 if(NOT CMAKE_CXX_STANDARD) set(CMAKE_CXX_STANDARD 14) endif() if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") add_compile_options(-Wall -Wextra -Wpedantic) endif() # 寻找依赖库（标准库） find_package(ament_cmake REQUIRED) find_package(rclcpp REQUIRED) find_package(sensor_msgs REQUIRED) # 寻找依赖库（外部库） find_package(Eigen3 REQUIRED) # 针对PCL库版本不适配会出现warning，做的补丁，其实没有解决问题 if(NOT DEFINED CMAKE_SUPPRESS_DEVELOPER_WARNINGS) set(CMAKE_SUPPRESS_DEVELOPER_WARNINGS 1 CACHE INTERNAL "No dev warnings") endif() find_package(PCL REQUIRED) find_package(rviz2 REQUIRED) find_package(OpenCV 3.2.0 REQUIRED) # 添加包含路径,方便include include_directories( /usr/include ${EIGEN3_INCLUDE_DIRS} ${PCL_INCLUDE_DIRS} ${RVIZ2_INCLUDE_DIRS} /usr/include/OGRE ${OpenCV_INCLUDE_DIRS} ) # 添加可执行文件 add_executable(reg_pcl src/reg_pcl.cpp) # 标准库需要使用ament_target_dependencies ament_target_dependencies(reg_pcl rclcpp sensor_msgs) # 非标准库需要使用target_link_libraries target_link_libraries(reg_pcl ${PCL_LIBRARIES} # ${OpenCV_LIBS} ) add_executable(img2pt src/img2pt.cpp) ament_target_dependencies(img2pt rclcpp sensor_msgs) target_link_libraries(img2pt ${PCL_LIBRARIES} ${OpenCV_LIBS} ) add_executable(odom_pre src/odom_pre.cpp) ament_target_dependencies(odom_pre rclcpp sensor_msgs) # target_link_libraries(odom_pre # ${PCL_LIBRARIES} # ${OpenCV_LIBS} # ) # add_executable(pt_show src/pt_show.cpp) # ament_target_dependencies(pt_show rclcpp sensor_msgs) # target_link_libraries(pt_show # ${PCL_LIBRARIES} # ${RVIZ2_LIBRARIES} # # ${OpenCV_LIBS} # ) ##安装可执行文件 install(TARGETS reg_pcl img2pt odom_pre # pt_show # EXPORT export_${PROJECT_NAME} DESTINATION lib/${PROJECT_NAME} ) ##########配置文件######### # 安装launch文件,无论该功能包的launch目录下有多少个launch文件，launch相关配置只需要设置一次即可。ros2 launch <包名> <launch文件名，无需带上launch文件夹地址> # 配置文件config同理 # 如果由rviz配置文件也可以同理，或者指定固定位置的文件 # 使用的config文件或者launch文件并不是src文件内的pcl_reg包内的config/launch文件，而是通过install复制到/install/pcl_reg/share/pcl_reg/config文件，，或者，，/install/pcl_reg/share/pcl_reg/launch文件 install(DIRECTORY launch config rviz2 DESTINATION share/${PROJECT_NAME}/ ) if(BUILD_TESTING) find_package(ament_lint_auto REQUIRED) ament_lint_auto_find_test_dependencies() endif() ament_package()
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对package.xml进行适当修改
<?xml version="1.0"?> <?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?> <package format="3"> <name>pcl_reg</name> <version>0.0.0</version> <description>TODO: Package description</description> <maintainer email="apollo@todo.todo">apollo</maintainer> <license>TODO: License declaration</license> <depend>rclcpp</depend> <depend>sensor_msgs</depend> <depend>EIGEN3</depend> <depend>PCL</depend> <depend>OpenCV</depend> <!-- ros2 launch 使得launch file可以被识别 --> <exec_depend>ros2launch</exec_depend> <buildtool_depend>ament_cmake</buildtool_depend> <test_depend>ament_lint_auto</test_depend> <test_depend>ament_lint_common</test_depend> <export> <build_type>ament_cmake</build_type> </export> </package>
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launch文件
img2pt.launch.py
使用方法 ： ros2 launch <包名> <launch文件名>
使用方法 ： ros2 launch pcl_reg img2pt.launch.py
from launch import LaunchDescription from launch_ros.actions import Node import os from ament_index_python.packages import get_package_share_directory def generate_launch_description(): config = os.path.join( #获取pcl_reg包的share文件夹的地址，使用的配置文件并不是src文件内的pcl_reg包内的config文件，而是通过install复制到/install/pcl_reg/share/pcl_reg/config文件 get_package_share_directory('pcl_reg'), 'config', 'img2pt.yaml' ) return LaunchDescription([ Node( #package包名，node_executable可执行文件名，（name节点名（可选）），至少提供这两个才能使launch文件运行 package='pcl_reg', node_executable='img2pt', name='point_cloud_publisher', #普通运行ros2 run，代码中的log或者cout可以打印到屏幕上；使用ros2 launch运行的话，默认打印到屏幕上的输出被关闭了。需要手动设置打开：output/emulate_tty output="screen", emulate_tty=True, parameters=[config], # 参数可以使用下面写法，直接在launch文件中设置，也可以通过yaml文件配置，例如上面 # parameters=[ # {"voxel_grid_x":0.08}, # {"voxel_grid_y":0.12}, # {"voxel_grid_z":0.66} # ] ), ])
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reg_pcl.launch.py
使用方法 ： ros2 launch pcl_reg reg_pcl.launch.py
使用方法与py内容填写同上。注意格式即可
from launch import LaunchDescription from launch_ros.actions import Node import os from ament_index_python.packages import get_package_share_directory def generate_launch_description(): config = os.path.join( get_package_share_directory('pcl_reg'), 'config', 'reg_pcl.yaml' ) return LaunchDescription([ Node( package='pcl_reg', node_executable='reg_pcl', name='point_cloud_processor', output="screen", emulate_tty=True, parameters=[config], ), ])
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mapping.launch.py
简单使用launch文件将两个可执行文件同时启动。但是这样一个文件内涉及对两个可执行文件的配置，这不利于一个大型工程的管理。
from launch import LaunchDescription from launch_ros.actions import Node def generate_launch_description(): return LaunchDescription([ Node( package='pcl_reg', node_executable='img2pt', name='point_cloud_publisher', output="screen", emulate_tty=True, ), Node( package='pcl_reg', node_executable='reg_pcl', name='point_cloud_processor', output="screen", emulate_tty=True, ), ##ExecuteProcess是针对有终端输入的情况 # ExecuteProcess() ]) # def generate_launch_description(): # ld = LaunchDescription() # node1 = Node( # package='pcl_reg', # eloquent版本不能用executable，要使用node_executable # node_executable='img2pt', # # name='point_cloud_publisher', # # output='screen' # ) # ld.add_action(node1) # return ld
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showviz.launch.py
launch文件不仅可以启动自己编写的可执行文件，也可以启动ros2的工具，例如rviz2.这个工具可以查看机器人的运行状态，可以查看点云等。
其中查看点云需要配置点云的坐标系与代码中的frame_id保持一致，需要设置节点名与代码中的topic（create_publisher的参数）保持一致
如果想要自动化一点，可以通过保存rviz2的的config文件，来保证效果。
import os from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription from launch_ros.actions import Node def generate_launch_description(): rviz_config = os.path.join( get_package_share_directory('pcl_reg'), 'rviz2', 'map_pt_conf.rviz' ) # rviz_config = os.path.join( # '/home','apollo', # '.rviz2', # 'map_pt_conf.rviz' # ) #下面这个写法也可以直接在终端执行，不难推测出，终端可启动工具都可以用这种方式使用launch文件启动 return LaunchDescription([ Node( package='rviz2', node_executable='rviz2', # name='rviz2', arguments=['-d', rviz_config] ) ])
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mapping_sub.launch.py
对整个工程的启动代码做统一管理
import os from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription from launch.actions import IncludeLaunchDescription from launch.launch_description_sources import PythonLaunchDescriptionSource def generate_launch_description(): img2pt = IncludeLaunchDescription( PythonLaunchDescriptionSource([os.path.join( get_package_share_directory('pcl_reg'), 'launch'), '/img2pt.launch.py']) ) reg_pcl = IncludeLaunchDescription( PythonLaunchDescriptionSource([os.path.join( get_package_share_directory('pcl_reg'), 'launch'), '/reg_pcl.launch.py']) ) rviz_node = IncludeLaunchDescription( PythonLaunchDescriptionSource([os.path.join( get_package_share_directory('pcl_reg'), 'launch'), '/showviz.launch.py']) ) return LaunchDescription([ img2pt, reg_pcl, # rviz_node ])
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img2pt.yaml
point_cloud_publisher: #节点名、不是excutable文件名
ros__parameters: #固定形式
use_sim_time: false #是否使用仿真时间，一般为false
voxel_grid_x: 0.05
voxel_grid_y: 0.05
voxel_grid_z: 0.85
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reg_pcl.yaml
point_cloud_processor:
ros__parameters:
use_sim_time: false
use_icp_: true
icpMaximumIterations: 1 #100 int型请务必写整数
icpMaxCorrespondenceDistance: 1.0005 #double类型不要直接写1，要写带小数点的
icpTransformationEpsilon: 1e-10 #支持科学计数法
icpEuclideanFitnessEpsilon: 0.001
icpFitnessScoreThresh: 0.3
ndtTransformationEpsilon: 1e-10
ndtResolution: 0.1
ndtFitnessScoreThresh: 0.3
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