一.环境配置
1.创建个工作空间
mkdir -p ~/LIO-SAM/src
2.拉个代码
搞个zip代码放/LIO-SAM/src里(或者直接git就不需要创建工作空间了)：
https://github.com/sram-v/LIO-SAM-ROS2/tree/ros2
3.整个conda环境
省的配置错了都乱套了（去官网搞个具体的步骤不放出来了，conda官方网址https://www.anaconda.com/download）
conda create -n lio-sam-clean python=3.94.编译下，也可以先不编译直接装包
cd ~/LIO-SAM
colcon build --packages-select lio_sam一定会缺少很多包:
5.看有没有你缺的依赖
缺少spdlog
conda install -c conda-forge spdlog -y缺少console_bridge
# 从源码编译 console-bridge
cd ~
git clone https://github.com/ros/console_bridge.git
cd console_bridge
mkdir build && cd build
cmake -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX ..
make -j$(nproc)
make install
# 验证安装
pkg-config --modversion console-bridge
# 设置 PKG_CONFIG_PATH 包含 Conda 环境的 pkgconfig 目录
export PKG_CONFIG_PATH=$CONDA_PREFIX/lib/pkgconfig:$PKG_CONFIG_PATH
# 现在验证 console_bridge
pkg-config --modversion console_bridge缺少orocos_kdl
conda install -c conda-forge orocos-kdl=1.5.3 -y
pkg-config --modversion orocos_kdl缺少 pcl-config
conda install -c conda-forge pcl=1.15.1 -y
export PCL_DIR=$CONDA_PREFIX/share/pcl-1.15
cat > test_pcl_cmake.cmake << 'EOF'
find_package(PCL REQUIRED)
message(STATUS "PCL found: ${PCL_FOUND}")
message(STATUS "PCL version: ${PCL_VERSION}")
message(STATUS "PCL include directories: ${PCL_INCLUDE_DIRS}")
message(STATUS "PCL library directories: ${PCL_LIBRARY_DIRS}")
message(STATUS "PCL libraries: ${PCL_LIBRARIES}")
EOF
缺少eigen-config
# 安装 Eigen 3.4.0 最新稳定版本
conda install -c conda-forge eigen=3.4.0 -y
# 验证安装
find $CONDA_PREFIX -name "*Eigen*" -type f 2>/dev/null | head -10
# 创建 Eigen CMake 配置目录
mkdir -p $CONDA_PREFIX/share/eigen3/cmake
# 创建 EigenConfig.cmake 文件
cat > $CONDA_PREFIX/share/eigen3/cmake/EigenConfig.cmake << 'EOF'
# Eigen CMake 配置文件
set(Eigen_FOUND TRUE)
set(Eigen_INCLUDE_DIRS "$ENV{CONDA_PREFIX}/include/eigen3")
# 设置版本信息
set(Eigen_VERSION "3.4.0")
set(Eigen_VERSION_MAJOR 3)
set(Eigen_VERSION_MINOR 4)
set(Eigen_VERSION_PATCH 0)
# 创建导入目标
if(NOT TARGET Eigen3::Eigen)
add_library(Eigen3::Eigen INTERFACE IMPORTED)
set_target_properties(Eigen3::Eigen PROPERTIES
INTERFACE_INCLUDE_DIRECTORIES "${Eigen_INCLUDE_DIRS}"
)
endif()
if(NOT TARGET Eigen::Eigen)
add_library(Eigen::Eigen INTERFACE IMPORTED)
set_target_properties(Eigen::Eigen PROPERTIES
INTERFACE_INCLUDE_DIRECTORIES "${Eigen_INCLUDE_DIRS}"
)
endif()
message(STATUS "Eigen found: ${Eigen_INCLUDE_DIRS}")
EOF
# 创建 EigenConfigVersion.cmake 文件
cat > $CONDA_PREFIX/share/eigen3/cmake/EigenConfigVersion.cmake << 'EOF'
set(PACKAGE_VERSION "3.4.0")
# 检查版本兼容性
if(PACKAGE_VERSION VERSION_LESS PACKAGE_FIND_VERSION)
set(PACKAGE_VERSION_COMPATIBLE FALSE)
else()
set(PACKAGE_VERSION_COMPATIBLE TRUE)
if(PACKAGE_VERSION VERSION_EQUAL PACKAGE_FIND_VERSION)
set(PACKAGE_VERSION_EXACT TRUE)
endif()
endif()
EOF缺少empy
conda install -c conda-forge empy -y
# 验证安装
python -c "import em; print('empy 模块安装成功')"缺少catkin_pkg
conda install -c conda-forge catkin_pkg -y
python -c "import catkin_pkg; print('catkin_pkg模块安装成功')"缺少lark
conda install -c conda-forge lark -y
# 验证安装
python -c "import lark; print('lark 模块安装成功')"找不到 pcl_conversions/pcl_conversions.h
/opt/ros/humble/include/pcl_conversions/pcl_conversions/pcl_conversions.h，这是一个嵌套的目录结构。代码中引用的是 #include <pcl_conversions/pcl_conversions.h>，但实际文件在 pcl_conversions/pcl_conversions/ 子目录中。
echo "=== 创建正确的符号链接结构 ==="
# 清理之前的尝试
rm -rf $CONDA_PREFIX/include/pcl_conversions*
# 创建正确的目录结构
mkdir -p $CONDA_PREFIX/include/pcl_conversions
# 创建符号链接指向实际的 pcl_conversions.h
ln -sf /opt/ros/humble/include/pcl_conversions/pcl_conversions/pcl_conversions.h $CONDA_PREFIX/include/pcl_conversions/pcl_conversions.h
# 验证符号链接
echo "=== 验证符号链接 ==="
ls -la $CONDA_PREFIX/include/pcl_conversions/
find $CONDA_PREFIX/include -name "*pcl_conversions*" | head -10
# 测试包含
echo "=== 测试包含 ==="
cat > /tmp/test_fixed.cpp << 'EOF'
#include <pcl_conversions/pcl_conversions.h>
int main() {
printf("pcl_conversions.h 包含成功!\n");
return 0;
}
EOF这个环境配置就差不多了
二.编译代码
colcon build --packages-select lio_sam
三.运行数据集
数据集你们可以从https://github.com/sram-v/LIO-SAM-ROS2/tree/ros2这里下载对应的，我这使用的是https://pan.baidu.com/s/1hhHvn96uEsDYJNss3Z209Q 提取码：2478（是用的这里的哈，注明一下出处：https://gitee.com/janetmayle/Simple-LIO-SAM-ROS2#https://gitee.com/link?target=https%3A%2F%2Fpan.baidu.com%2Fs%2F1hhHvn96uEsDYJNss3Z209Q）
这个数据集的话题名和我们配置文件中的不一样哈，但没有关系，用我这个指令就可以了
ros2 bag play ./park_dataset/ --remap /points_raw:=/points /imu_raw:=/imu/data --clock --loop
四.运行程序
source ./install/setup.bash
ros2 launch lio_sam run.launch.pyrviz如下：
五.生成地图
修改/LIO-SAM/src/LIO-SAM-ros2/config中的params.yaml文件中的具体保存地图的目录，和上面一个参数
然后按crtl+c退出的时候可以生成地图，但如果你想调用服务在过程中保留图的话不太行，因为这个环境的原因，我这边作了一个改进，修改了LIO-SAM/src/LIO-SAM-ros2/src下的mapOptmization.cpp，现在可以进行发布
ros2 topic pub /lio_sam/save_map_trigger std_msgs/msg/String "{data: '/LIO-SAM/pcd'}" //这个是一直保存哈
ros2 topic pub -1 /lio_sam/save_map_trigger std_msgs/msg/String "{data: '/LIO-SAM/pcd'}" //这个是保存一次进行保存了，/LIO-SAM/pcd'是你想保存地图的路径，修改之后的mapOptmization.cpp如下（我想直接上传来着，刚绑定资源不知道搞哪去了，使用不太熟练，请多见谅），更换这个文件之后记得重新编译运行：
#include "utility.hpp"
#include "lio_sam/msg/cloud_info.hpp"
#include "lio_sam/srv/save_map.hpp"
#include <gtsam/geometry/Rot3.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/navigation/GPSFactor.h>
#include <gtsam/navigation/ImuFactor.h>
#include <gtsam/navigation/CombinedImuFactor.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/Marginals.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/nonlinear/ISAM2.h>
using namespace gtsam;
using symbol_shorthand::X; // Pose3 (x,y,z,r,p,y)
using symbol_shorthand::V; // Vel (xdot,ydot,zdot)
using symbol_shorthand::B; // Bias (ax,ay,az,gx,gy,gz)
using symbol_shorthand::G; // GPS pose
/*
* A point cloud type that has 6D pose info ([x,y,z,roll,pitch,yaw] intensity is time stamp)
*/
struct PointXYZIRPYT
{
PCL_ADD_POINT4D
PCL_ADD_INTENSITY; // preferred way of adding a XYZ+padding
float roll;
float pitch;
float yaw;
double time;
EIGEN_MAKE_ALIGNED_OPERATOR_NEW // make sure our new allocators are aligned
} EIGEN_ALIGN16; // enforce SSE padding for correct memory alignment
POINT_CLOUD_REGISTER_POINT_STRUCT (PointXYZIRPYT,
(float, x, x) (float, y, y)
(float, z, z) (float, intensity, intensity)
(float, roll, roll) (float, pitch, pitch) (float, yaw, yaw)
(double, time, time))
typedef PointXYZIRPYT PointTypePose;
class mapOptimization : public ParamServer
{
public:
// gtsam
NonlinearFactorGraph gtSAMgraph;
Values initialEstimate;
Values optimizedEstimate;
ISAM2 *isam;
Values isamCurrentEstimate;
Eigen::MatrixXd poseCovariance;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pubLaserCloudSurround;
rclcpp::Publisher<nav_msgs::msg::Odometry>::SharedPtr pubLaserOdometryGlobal;
rclcpp::Publisher<nav_msgs::msg::Odometry>::SharedPtr pubLaserOdometryIncremental;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pubKeyPoses;
rclcpp::Publisher<nav_msgs::msg::Path>::SharedPtr pubPath;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pubHistoryKeyFrames;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pubIcpKeyFrames;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pubRecentKeyFrames;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pubRecentKeyFrame;
rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr pubCloudRegisteredRaw;
rclcpp::Publisher<visualization_msgs::msg::MarkerArray>::SharedPtr pubLoopConstraintEdge;
rclcpp::Service<lio_sam::srv::SaveMap>::SharedPtr srvSaveMap;
rclcpp::Subscription<lio_sam::msg::CloudInfo>::SharedPtr subCloud;
rclcpp::Subscription<nav_msgs::msg::Odometry>::SharedPtr subGPS;
rclcpp::Subscription<std_msgs::msg::Float64MultiArray>::SharedPtr subLoop;
// 添加保存地图的话题订阅
rclcpp::Subscription<std_msgs::msg::String>::SharedPtr subSaveMapTrigger;
std::deque<nav_msgs::msg::Odometry> gpsQueue;
lio_sam::msg::CloudInfo cloudInfo;
vector<pcl::PointCloud<PointType>::Ptr> cornerCloudKeyFrames;
vector<pcl::PointCloud<PointType>::Ptr> surfCloudKeyFrames;
pcl::PointCloud<PointType>::Ptr cloudKeyPoses3D;
pcl::PointCloud<PointTypePose>::Ptr cloudKeyPoses6D;
pcl::PointCloud<PointType>::Ptr copy_cloudKeyPoses3D;
pcl::PointCloud<PointTypePose>::Ptr copy_cloudKeyPoses6D;
pcl::PointCloud<PointType>::Ptr laserCloudCornerLast; // corner feature set from odoOptimization
pcl::PointCloud<PointType>::Ptr laserCloudSurfLast; // surf feature set from odoOptimization
pcl::PointCloud<PointType>::Ptr laserCloudCornerLastDS; // downsampled corner feature set from odoOptimization
pcl::PointCloud<PointType>::Ptr laserCloudSurfLastDS; // downsampled surf feature set from odoOptimization
pcl::PointCloud<PointType>::Ptr laserCloudOri;
pcl::PointCloud<PointType>::Ptr coeffSel;
std::vector<PointType> laserCloudOriCornerVec; // corner point holder for parallel computation
std::vector<PointType> coeffSelCornerVec;
std::vector<bool> laserCloudOriCornerFlag;
std::vector<PointType> laserCloudOriSurfVec; // surf point holder for parallel computation
std::vector<PointType> coeffSelSurfVec;
std::vector<bool> laserCloudOriSurfFlag;
map<int, pair<pcl::PointCloud<PointType>, pcl::PointCloud<PointType>>> laserCloudMapContainer;
pcl::PointCloud<PointType>::Ptr laserCloudCornerFromMap;
pcl::PointCloud<PointType>::Ptr laserCloudSurfFromMap;
pcl::PointCloud<PointType>::Ptr laserCloudCornerFromMapDS;
pcl::PointCloud<PointType>::Ptr laserCloudSurfFromMapDS;
pcl::KdTreeFLANN<PointType>::Ptr kdtreeCornerFromMap;
pcl::KdTreeFLANN<PointType>::Ptr kdtreeSurfFromMap;
pcl::KdTreeFLANN<PointType>::Ptr kdtreeSurroundingKeyPoses;
pcl::KdTreeFLANN<PointType>::Ptr kdtreeHistoryKeyPoses;
pcl::VoxelGrid<PointType> downSizeFilterCorner;
pcl::VoxelGrid<PointType> downSizeFilterSurf;
pcl::VoxelGrid<PointType> downSizeFilterICP;
pcl::VoxelGrid<PointType> downSizeFilterSurroundingKeyPoses; // for surrounding key poses of scan-to-map optimization
rclcpp::Time timeLaserInfoStamp;
double timeLaserInfoCur;
float transformTobeMapped[6];
std::mutex mtx;
std::mutex mtxLoopInfo;
bool isDegenerate = false;
Eigen::Matrix<float, 6, 6> matP;
int laserCloudCornerFromMapDSNum = 0;
int laserCloudSurfFromMapDSNum = 0;
int laserCloudCornerLastDSNum = 0;
int laserCloudSurfLastDSNum = 0;
bool aLoopIsClosed = false;
map<int, int> loopIndexContainer; // from new to old
vector<pair<int, int>> loopIndexQueue;
vector<gtsam::Pose3> loopPoseQueue;
vector<gtsam::noiseModel::Diagonal::shared_ptr> loopNoiseQueue;
deque<std_msgs::msg::Float64MultiArray> loopInfoVec;
nav_msgs::msg::Path globalPath;
Eigen::Affine3f transPointAssociateToMap;
Eigen::Affine3f incrementalOdometryAffineFront;
Eigen::Affine3f incrementalOdometryAffineBack;
std::unique_ptr<tf2_ros::TransformBroadcaster> br;
// 添加保存地图的线程和变量
std::thread saveMapThread;
std::atomic<bool> saveMapRequested{false};
std::string requestedSavePath;
float requestedResolution = 0.0f;
mapOptimization(const rclcpp::NodeOptions & options) : ParamServer("lio_sam_mapOptimization", options)
{
ISAM2Params parameters;
parameters.relinearizeThreshold = 0.1;
parameters.relinearizeSkip = 1;
isam = new ISAM2(parameters);
pubKeyPoses = create_publisher<sensor_msgs::msg::PointCloud2>("lio_sam/mapping/trajectory", 1);
pubLaserCloudSurround = create_publisher<sensor_msgs::msg::PointCloud2>("lio_sam/mapping/map_global", 1);
pubLaserOdometryGlobal = create_publisher<nav_msgs::msg::Odometry>("lio_sam/mapping/odometry", qos);
pubLaserOdometryIncremental = create_publisher<nav_msgs::msg::Odometry>(
"lio_sam/mapping/odometry_incremental", qos);
pubPath = create_publisher<nav_msgs::msg::Path>("lio_sam/mapping/path", 1);
br = std::make_unique<tf2_ros::TransformBroadcaster>(this);
subCloud = create_subscription<lio_sam::msg::CloudInfo>(
"lio_sam/feature/cloud_info", qos,
std::bind(&mapOptimization::laserCloudInfoHandler, this, std::placeholders::_1));
subGPS = create_subscription<nav_msgs::msg::Odometry>(
gpsTopic, 200,
std::bind(&mapOptimization::gpsHandler, this, std::placeholders::_1));
subLoop = create_subscription<std_msgs::msg::Float64MultiArray>(
"lio_loop/loop_closure_detection", qos,
std::bind(&mapOptimization::loopInfoHandler, this, std::placeholders::_1));
// 添加保存地图触发话题订阅
subSaveMapTrigger = create_subscription<std_msgs::msg::String>(
"lio_sam/save_map_trigger", 10,
std::bind(&mapOptimization::saveMapTriggerHandler, this, std::placeholders::_1));
auto saveMapService = [this](const std::shared_ptr<rmw_request_id_t> request_header, const std::shared_ptr<lio_sam::srv::SaveMap::Request> req, std::shared_ptr<lio_sam::srv::SaveMap::Response> res) -> void {
(void)request_header;
string saveMapDirectory;
cout << "****************************************************" << endl;
cout << "Saving map to pcd files ..." << endl;
if(req->destination.empty()) saveMapDirectory = std::getenv("HOME") + savePCDDirectory;
else saveMapDirectory = std::getenv("HOME") + req->destination;
cout << "Save destination: " << saveMapDirectory << endl;
// create directory and remove old files;
int unused = system((std::string("exec rm -r ") + saveMapDirectory).c_str());
unused = system((std::string("mkdir -p ") + saveMapDirectory).c_str());
// save key frame transformations
pcl::io::savePCDFileBinary(saveMapDirectory + "/trajectory.pcd", *cloudKeyPoses3D);
pcl::io::savePCDFileBinary(saveMapDirectory + "/transformations.pcd", *cloudKeyPoses6D);
// extract global point cloud map
pcl::PointCloud<PointType>::Ptr globalCornerCloud(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalCornerCloudDS(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalSurfCloud(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalSurfCloudDS(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalMapCloud(new pcl::PointCloud<PointType>());
for (int i = 0; i < (int)cloudKeyPoses3D->size(); i++)
{
*globalCornerCloud += *transformPointCloud(cornerCloudKeyFrames[i], &cloudKeyPoses6D->points[i]);
*globalSurfCloud += *transformPointCloud(surfCloudKeyFrames[i], &cloudKeyPoses6D->points[i]);
cout << "\r" << std::flush << "Processing feature cloud " << i << " of " << cloudKeyPoses6D->size() << " ...";
}
if(req->resolution != 0)
{
cout << "\n\nSave resolution: " << req->resolution << endl;
// down-sample and save corner cloud
downSizeFilterCorner.setInputCloud(globalCornerCloud);
downSizeFilterCorner.setLeafSize(req->resolution, req->resolution, req->resolution);
downSizeFilterCorner.filter(*globalCornerCloudDS);
pcl::io::savePCDFileBinary(saveMapDirectory + "/CornerMap.pcd", *globalCornerCloudDS);
// down-sample and save surf cloud
downSizeFilterSurf.setInputCloud(globalSurfCloud);
downSizeFilterSurf.setLeafSize(req->resolution, req->resolution, req->resolution);
downSizeFilterSurf.filter(*globalSurfCloudDS);
pcl::io::savePCDFileBinary(saveMapDirectory + "/SurfMap.pcd", *globalSurfCloudDS);
}
else
{
// save corner cloud
pcl::io::savePCDFileBinary(saveMapDirectory + "/CornerMap.pcd", *globalCornerCloud);
// save surf cloud
pcl::io::savePCDFileBinary(saveMapDirectory + "/SurfMap.pcd", *globalSurfCloud);
}
// save global point cloud map
*globalMapCloud += *globalCornerCloud;
*globalMapCloud += *globalSurfCloud;
int ret = pcl::io::savePCDFileBinary(saveMapDirectory + "/GlobalMap.pcd", *globalMapCloud);
res->success = ret == 0;
downSizeFilterCorner.setLeafSize(mappingCornerLeafSize, mappingCornerLeafSize, mappingCornerLeafSize);
downSizeFilterSurf.setLeafSize(mappingSurfLeafSize, mappingSurfLeafSize, mappingSurfLeafSize);
cout << "****************************************************" << endl;
cout << "Saving map to pcd files completed\n" << endl;
return;
};
srvSaveMap = create_service<lio_sam::srv::SaveMap>("lio_sam/save_map", saveMapService);
pubHistoryKeyFrames = create_publisher<sensor_msgs::msg::PointCloud2>("lio_sam/mapping/icp_loop_closure_history_cloud", 1);
pubIcpKeyFrames = create_publisher<sensor_msgs::msg::PointCloud2>("lio_sam/mapping/icp_loop_closure_history_cloud", 1);
pubLoopConstraintEdge = create_publisher<visualization_msgs::msg::MarkerArray>("/lio_sam/mapping/loop_closure_constraints", 1);
pubRecentKeyFrames = create_publisher<sensor_msgs::msg::PointCloud2>("lio_sam/mapping/map_local", 1);
pubRecentKeyFrame = create_publisher<sensor_msgs::msg::PointCloud2>("lio_sam/mapping/cloud_registered", 1);
pubCloudRegisteredRaw = create_publisher<sensor_msgs::msg::PointCloud2>("lio_sam/mapping/cloud_registered_raw", 1);
downSizeFilterCorner.setLeafSize(mappingCornerLeafSize, mappingCornerLeafSize, mappingCornerLeafSize);
downSizeFilterSurf.setLeafSize(mappingSurfLeafSize, mappingSurfLeafSize, mappingSurfLeafSize);
downSizeFilterICP.setLeafSize(mappingSurfLeafSize, mappingSurfLeafSize, mappingSurfLeafSize);
downSizeFilterSurroundingKeyPoses.setLeafSize(surroundingKeyframeDensity, surroundingKeyframeDensity, surroundingKeyframeDensity); // for surrounding key poses of scan-to-map optimization
allocateMemory();
}
// 保存地图的线程函数
void saveMapInThread(const std::string& directory, float resolution)
{
cout << "****************************************************" << endl;
cout << "Saving map to pcd files in thread..." << endl;
cout << "Save destination: " << directory << endl;
// 创建目录并删除旧文件
int unused = system((std::string("exec rm -r ") + directory).c_str());
unused = system((std::string("mkdir -p ") + directory).c_str());
// 保存关键帧轨迹
pcl::io::savePCDFileBinary(directory + "/trajectory.pcd", *cloudKeyPoses3D);
pcl::io::savePCDFileBinary(directory + "/transformations.pcd", *cloudKeyPoses6D);
// 提取全局点云地图
pcl::PointCloud<PointType>::Ptr globalCornerCloud(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalCornerCloudDS(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalSurfCloud(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalSurfCloudDS(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalMapCloud(new pcl::PointCloud<PointType>());
for (int i = 0; i < (int)cloudKeyPoses3D->size(); i++)
{
*globalCornerCloud += *transformPointCloud(cornerCloudKeyFrames[i], &cloudKeyPoses6D->points[i]);
*globalSurfCloud += *transformPointCloud(surfCloudKeyFrames[i], &cloudKeyPoses6D->points[i]);
cout << "\r" << std::flush << "Processing feature cloud " << i << " of " << cloudKeyPoses6D->size() << " ...";
}
if(resolution != 0)
{
cout << "\n\nSave resolution: " << resolution << endl;
// 下采样并保存角点云
downSizeFilterCorner.setInputCloud(globalCornerCloud);
downSizeFilterCorner.setLeafSize(resolution, resolution, resolution);
downSizeFilterCorner.filter(*globalCornerCloudDS);
pcl::io::savePCDFileBinary(directory + "/CornerMap.pcd", *globalCornerCloudDS);
// 下采样并保存面点云
downSizeFilterSurf.setInputCloud(globalSurfCloud);
downSizeFilterSurf.setLeafSize(resolution, resolution, resolution);
downSizeFilterSurf.filter(*globalSurfCloudDS);
pcl::io::savePCDFileBinary(directory + "/SurfMap.pcd", *globalSurfCloudDS);
}
else
{
// 保存角点云
pcl::io::savePCDFileBinary(directory + "/CornerMap.pcd", *globalCornerCloud);
// 保存面点云
pcl::io::savePCDFileBinary(directory + "/SurfMap.pcd", *globalSurfCloud);
}
// 保存全局点云地图
*globalMapCloud += *globalCornerCloud;
*globalMapCloud += *globalSurfCloud;
pcl::io::savePCDFileBinary(directory + "/GlobalMap.pcd", *globalMapCloud);
// 重置滤波器参数
downSizeFilterCorner.setLeafSize(mappingCornerLeafSize, mappingCornerLeafSize, mappingCornerLeafSize);
downSizeFilterSurf.setLeafSize(mappingSurfLeafSize, mappingSurfLeafSize, mappingSurfLeafSize);
cout << "****************************************************" << endl;
cout << "Saving map to pcd files completed\n" << endl;
}
// 处理保存地图触发消息
void saveMapTriggerHandler(const std_msgs::msg::String::SharedPtr msg)
{
RCLCPP_INFO(get_logger(), "Received save map trigger: %s", msg->data.c_str());
// 解析消息内容，格式为 "路径:分辨率" 或仅 "路径"
std::string data = msg->data;
std::string path;
float resolution = 0.0f;
size_t colon_pos = data.find(':');
if (colon_pos != std::string::npos) {
path = data.substr(0, colon_pos);
try {
resolution = std::stof(data.substr(colon_pos + 1));
} catch (const std::exception& e) {
RCLCPP_WARN(get_logger(), "Invalid resolution format, using default 0.0");
}
} else {
path = data;
}
// 如果路径为空，使用默认路径
if (path.empty()) {
path = savePCDDirectory;
}
// 构建完整路径
const char* home_dir = std::getenv("HOME");
if (!home_dir) {
RCLCPP_ERROR(get_logger(), "HOME environment variable not set!");
return;
}
std::string full_path = std::string(home_dir) + path;
RCLCPP_INFO(get_logger(), "Starting save map thread for path: %s", full_path.c_str());
// 直接启动保存线程，不等待激光数据
if (saveMapThread.joinable()) {
saveMapThread.join();
}
saveMapThread = std::thread(&mapOptimization::saveMapInThread, this, full_path, resolution);
RCLCPP_INFO(get_logger(), "Save map thread started");
}
void allocateMemory()
{
cloudKeyPoses3D.reset(new pcl::PointCloud<PointType>());
cloudKeyPoses6D.reset(new pcl::PointCloud<PointTypePose>());
copy_cloudKeyPoses3D.reset(new pcl::PointCloud<PointType>());
copy_cloudKeyPoses6D.reset(new pcl::PointCloud<PointTypePose>());
kdtreeSurroundingKeyPoses.reset(new pcl::KdTreeFLANN<PointType>());
kdtreeHistoryKeyPoses.reset(new pcl::KdTreeFLANN<PointType>());
laserCloudCornerLast.reset(new pcl::PointCloud<PointType>()); // corner feature set from odoOptimization
laserCloudSurfLast.reset(new pcl::PointCloud<PointType>()); // surf feature set from odoOptimization
laserCloudCornerLastDS.reset(new pcl::PointCloud<PointType>()); // downsampled corner featuer set from odoOptimization
laserCloudSurfLastDS.reset(new pcl::PointCloud<PointType>()); // downsampled surf featuer set from odoOptimization
laserCloudOri.reset(new pcl::PointCloud<PointType>());
coeffSel.reset(new pcl::PointCloud<PointType>());
laserCloudOriCornerVec.resize(N_SCAN * Horizon_SCAN);
coeffSelCornerVec.resize(N_SCAN * Horizon_SCAN);
laserCloudOriCornerFlag.resize(N_SCAN * Horizon_SCAN);
laserCloudOriSurfVec.resize(N_SCAN * Horizon_SCAN);
coeffSelSurfVec.resize(N_SCAN * Horizon_SCAN);
laserCloudOriSurfFlag.resize(N_SCAN * Horizon_SCAN);
std::fill(laserCloudOriCornerFlag.begin(), laserCloudOriCornerFlag.end(), false);
std::fill(laserCloudOriSurfFlag.begin(), laserCloudOriSurfFlag.end(), false);
laserCloudCornerFromMap.reset(new pcl::PointCloud<PointType>());
laserCloudSurfFromMap.reset(new pcl::PointCloud<PointType>());
laserCloudCornerFromMapDS.reset(new pcl::PointCloud<PointType>());
laserCloudSurfFromMapDS.reset(new pcl::PointCloud<PointType>());
kdtreeCornerFromMap.reset(new pcl::KdTreeFLANN<PointType>());
kdtreeSurfFromMap.reset(new pcl::KdTreeFLANN<PointType>());
for (int i = 0; i < 6; ++i){
transformTobeMapped[i] = 0;
}
matP.setZero();
}
void laserCloudInfoHandler(const lio_sam::msg::CloudInfo::SharedPtr msgIn)
{
// extract time stamp
timeLaserInfoStamp = msgIn->header.stamp;
timeLaserInfoCur = stamp2Sec(msgIn->header.stamp);
// extract info and feature cloud
cloudInfo = *msgIn;
pcl::fromROSMsg(msgIn->cloud_corner, *laserCloudCornerLast);
pcl::fromROSMsg(msgIn->cloud_surface, *laserCloudSurfLast);
std::lock_guard<std::mutex> lock(mtx);
static double timeLastProcessing = -1;
if (timeLaserInfoCur - timeLastProcessing >= mappingProcessInterval)
{
timeLastProcessing = timeLaserInfoCur;
updateInitialGuess();
extractSurroundingKeyFrames();
downsampleCurrentScan();
scan2MapOptimization();
saveKeyFramesAndFactor();
correctPoses();
publishOdometry();
publishFrames();
// 检查保存地图请求
//checkSaveMapRequest();
}
}
void gpsHandler(const nav_msgs::msg::Odometry::SharedPtr gpsMsg)
{
gpsQueue.push_back(*gpsMsg);
}
void pointAssociateToMap(PointType const * const pi, PointType * const po)
{
po->x = transPointAssociateToMap(0,0) * pi->x + transPointAssociateToMap(0,1) * pi->y + transPointAssociateToMap(0,2) * pi->z + transPointAssociateToMap(0,3);
po->y = transPointAssociateToMap(1,0) * pi->x + transPointAssociateToMap(1,1) * pi->y + transPointAssociateToMap(1,2) * pi->z + transPointAssociateToMap(1,3);
po->z = transPointAssociateToMap(2,0) * pi->x + transPointAssociateToMap(2,1) * pi->y + transPointAssociateToMap(2,2) * pi->z + transPointAssociateToMap(2,3);
po->intensity = pi->intensity;
}
pcl::PointCloud<PointType>::Ptr transformPointCloud(pcl::PointCloud<PointType>::Ptr cloudIn, PointTypePose* transformIn)
{
pcl::PointCloud<PointType>::Ptr cloudOut(new pcl::PointCloud<PointType>());
int cloudSize = cloudIn->size();
cloudOut->resize(cloudSize);
Eigen::Affine3f transCur = pcl::getTransformation(transformIn->x, transformIn->y, transformIn->z, transformIn->roll, transformIn->pitch, transformIn->yaw);
#pragma omp parallel for num_threads(numberOfCores)
for (int i = 0; i < cloudSize; ++i)
{
const auto &pointFrom = cloudIn->points[i];
cloudOut->points[i].x = transCur(0,0) * pointFrom.x + transCur(0,1) * pointFrom.y + transCur(0,2) * pointFrom.z + transCur(0,3);
cloudOut->points[i].y = transCur(1,0) * pointFrom.x + transCur(1,1) * pointFrom.y + transCur(1,2) * pointFrom.z + transCur(1,3);
cloudOut->points[i].z = transCur(2,0) * pointFrom.x + transCur(2,1) * pointFrom.y + transCur(2,2) * pointFrom.z + transCur(2,3);
cloudOut->points[i].intensity = pointFrom.intensity;
}
return cloudOut;
}
gtsam::Pose3 pclPointTogtsamPose3(PointTypePose thisPoint)
{
return gtsam::Pose3(gtsam::Rot3::RzRyRx(double(thisPoint.roll), double(thisPoint.pitch), double(thisPoint.yaw)),
gtsam::Point3(double(thisPoint.x), double(thisPoint.y), double(thisPoint.z)));
}
gtsam::Pose3 trans2gtsamPose(float transformIn[])
{
return gtsam::Pose3(gtsam::Rot3::RzRyRx(transformIn[0], transformIn[1], transformIn[2]),
gtsam::Point3(transformIn[3], transformIn[4], transformIn[5]));
}
Eigen::Affine3f pclPointToAffine3f(PointTypePose thisPoint)
{
return pcl::getTransformation(thisPoint.x, thisPoint.y, thisPoint.z, thisPoint.roll, thisPoint.pitch, thisPoint.yaw);
}
Eigen::Affine3f trans2Affine3f(float transformIn[])
{
return pcl::getTransformation(transformIn[3], transformIn[4], transformIn[5], transformIn[0], transformIn[1], transformIn[2]);
}
PointTypePose trans2PointTypePose(float transformIn[])
{
PointTypePose thisPose6D;
thisPose6D.x = transformIn[3];
thisPose6D.y = transformIn[4];
thisPose6D.z = transformIn[5];
thisPose6D.roll = transformIn[0];
thisPose6D.pitch = transformIn[1];
thisPose6D.yaw = transformIn[2];
return thisPose6D;
}
void visualizeGlobalMapThread()
{
rclcpp::Rate rate(0.2);
while (rclcpp::ok()){
rate.sleep();
publishGlobalMap();
}
if (savePCD == false)
return;
cout << "****************************************************" << endl;
cout << "Saving map to pcd files ..." << endl;
savePCDDirectory = std::getenv("HOME") + savePCDDirectory;
int unused = system((std::string("exec rm -r ") + savePCDDirectory).c_str());
unused = system((std::string("mkdir ") + savePCDDirectory).c_str());
pcl::io::savePCDFileASCII(savePCDDirectory + "trajectory.pcd", *cloudKeyPoses3D);
pcl::io::savePCDFileASCII(savePCDDirectory + "transformations.pcd", *cloudKeyPoses6D);
pcl::PointCloud<PointType>::Ptr globalCornerCloud(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalCornerCloudDS(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalSurfCloud(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalSurfCloudDS(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalMapCloud(new pcl::PointCloud<PointType>());
for (int i = 0; i < (int)cloudKeyPoses3D->size(); i++) {
*globalCornerCloud += *transformPointCloud(cornerCloudKeyFrames[i], &cloudKeyPoses6D->points[i]);
*globalSurfCloud += *transformPointCloud(surfCloudKeyFrames[i], &cloudKeyPoses6D->points[i]);
cout << "\r" << std::flush << "Processing feature cloud " << i << " of " << cloudKeyPoses6D->size() << " ...";
}
downSizeFilterCorner.setInputCloud(globalCornerCloud);
downSizeFilterCorner.filter(*globalCornerCloudDS);
pcl::io::savePCDFileASCII(savePCDDirectory + "cloudCorner.pcd", *globalCornerCloudDS);
downSizeFilterSurf.setInputCloud(globalSurfCloud);
downSizeFilterSurf.filter(*globalSurfCloudDS);
pcl::io::savePCDFileASCII(savePCDDirectory + "cloudSurf.pcd", *globalSurfCloudDS);
*globalMapCloud += *globalCornerCloud;
*globalMapCloud += *globalSurfCloud;
pcl::io::savePCDFileASCII(savePCDDirectory + "cloudGlobal.pcd", *globalMapCloud);
cout << "****************************************************" << endl;
cout << "Saving map to pcd files completed" << endl;
}
void publishGlobalMap()
{
if (pubLaserCloudSurround->get_subscription_count() == 0)
return;
if (cloudKeyPoses3D->points.empty() == true)
return;
pcl::KdTreeFLANN<PointType>::Ptr kdtreeGlobalMap(new pcl::KdTreeFLANN<PointType>());;
pcl::PointCloud<PointType>::Ptr globalMapKeyPoses(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalMapKeyPosesDS(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalMapKeyFrames(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr globalMapKeyFramesDS(new pcl::PointCloud<PointType>());
// kd-tree to find near key frames to visualize
std::vector<int> pointSearchIndGlobalMap;
std::vector<float> pointSearchSqDisGlobalMap;
// search near key frames to visualize
mtx.lock();
kdtreeGlobalMap->setInputCloud(cloudKeyPoses3D);
kdtreeGlobalMap->radiusSearch(cloudKeyPoses3D->back(), globalMapVisualizationSearchRadius, pointSearchIndGlobalMap, pointSearchSqDisGlobalMap, 0);
mtx.unlock();
for (int i = 0; i < (int)pointSearchIndGlobalMap.size(); ++i)
globalMapKeyPoses->push_back(cloudKeyPoses3D->points[pointSearchIndGlobalMap[i]]);
// downsample near selected key frames
pcl::VoxelGrid<PointType> downSizeFilterGlobalMapKeyPoses; // for global map visualization
downSizeFilterGlobalMapKeyPoses.setLeafSize(globalMapVisualizationPoseDensity, globalMapVisualizationPoseDensity, globalMapVisualizationPoseDensity); // for global map visualization
downSizeFilterGlobalMapKeyPoses.setInputCloud(globalMapKeyPoses);
downSizeFilterGlobalMapKeyPoses.filter(*globalMapKeyPosesDS);
for(auto& pt : globalMapKeyPosesDS->points)
{
kdtreeGlobalMap->nearestKSearch(pt, 1, pointSearchIndGlobalMap, pointSearchSqDisGlobalMap);
pt.intensity = cloudKeyPoses3D->points[pointSearchIndGlobalMap[0]].intensity;
}
// extract visualized and downsampled key frames
for (int i = 0; i < (int)globalMapKeyPosesDS->size(); ++i){
if (pointDistance(globalMapKeyPosesDS->points[i], cloudKeyPoses3D->back()) > globalMapVisualizationSearchRadius)
continue;
int thisKeyInd = (int)globalMapKeyPosesDS->points[i].intensity;
*globalMapKeyFrames += *transformPointCloud(cornerCloudKeyFrames[thisKeyInd], &cloudKeyPoses6D->points[thisKeyInd]);
*globalMapKeyFrames += *transformPointCloud(surfCloudKeyFrames[thisKeyInd], &cloudKeyPoses6D->points[thisKeyInd]);
}
// downsample visualized points
pcl::VoxelGrid<PointType> downSizeFilterGlobalMapKeyFrames; // for global map visualization
downSizeFilterGlobalMapKeyFrames.setLeafSize(globalMapVisualizationLeafSize, globalMapVisualizationLeafSize, globalMapVisualizationLeafSize); // for global map visualization
downSizeFilterGlobalMapKeyFrames.setInputCloud(globalMapKeyFrames);
downSizeFilterGlobalMapKeyFrames.filter(*globalMapKeyFramesDS);
publishCloud(pubLaserCloudSurround, globalMapKeyFramesDS, timeLaserInfoStamp, odometryFrame);
}
void loopClosureThread()
{
if (loopClosureEnableFlag == false)
return;
rclcpp::Rate rate(loopClosureFrequency);
while (rclcpp::ok())
{
rate.sleep();
performLoopClosure();
visualizeLoopClosure();
}
}
void loopInfoHandler(const std_msgs::msg::Float64MultiArray::SharedPtr loopMsg)
{
std::lock_guard<std::mutex> lock(mtxLoopInfo);
if (loopMsg->data.size() != 2)
return;
loopInfoVec.push_back(*loopMsg);
while (loopInfoVec.size() > 5)
loopInfoVec.pop_front();
}
void performLoopClosure()
{
if (cloudKeyPoses3D->points.empty() == true)
return;
mtx.lock();
*copy_cloudKeyPoses3D = *cloudKeyPoses3D;
*copy_cloudKeyPoses6D = *cloudKeyPoses6D;
mtx.unlock();
// find keys
int loopKeyCur;
int loopKeyPre;
if (detectLoopClosureExternal(&loopKeyCur, &loopKeyPre) == false)
if (detectLoopClosureDistance(&loopKeyCur, &loopKeyPre) == false)
return;
// extract cloud
pcl::PointCloud<PointType>::Ptr cureKeyframeCloud(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr prevKeyframeCloud(new pcl::PointCloud<PointType>());
{
loopFindNearKeyframes(cureKeyframeCloud, loopKeyCur, 0);
loopFindNearKeyframes(prevKeyframeCloud, loopKeyPre, historyKeyframeSearchNum);
if (cureKeyframeCloud->size() < 300 || prevKeyframeCloud->size() < 1000)
return;
if (pubHistoryKeyFrames->get_subscription_count() != 0)
publishCloud(pubHistoryKeyFrames, prevKeyframeCloud, timeLaserInfoStamp, odometryFrame);
}
// ICP Settings
static pcl::IterativeClosestPoint<PointType, PointType> icp;
icp.setMaxCorrespondenceDistance(historyKeyframeSearchRadius*2);
icp.setMaximumIterations(100);
icp.setTransformationEpsilon(1e-6);
icp.setEuclideanFitnessEpsilon(1e-6);
icp.setRANSACIterations(0);
// Align clouds
icp.setInputSource(cureKeyframeCloud);
icp.setInputTarget(prevKeyframeCloud);
pcl::PointCloud<PointType>::Ptr unused_result(new pcl::PointCloud<PointType>());
icp.align(*unused_result);
if (icp.hasConverged() == false || icp.getFitnessScore() > historyKeyframeFitnessScore)
return;
// publish corrected cloud
if (pubIcpKeyFrames->get_subscription_count() != 0)
{
pcl::PointCloud<PointType>::Ptr closed_cloud(new pcl::PointCloud<PointType>());
pcl::transformPointCloud(*cureKeyframeCloud, *closed_cloud, icp.getFinalTransformation());
publishCloud(pubIcpKeyFrames, closed_cloud, timeLaserInfoStamp, odometryFrame);
}
// Get pose transformation
float x, y, z, roll, pitch, yaw;
Eigen::Affine3f correctionLidarFrame;
correctionLidarFrame = icp.getFinalTransformation();
// transform from world origin to wrong pose
Eigen::Affine3f tWrong = pclPointToAffine3f(copy_cloudKeyPoses6D->points[loopKeyCur]);
// transform from world origin to corrected pose
Eigen::Affine3f tCorrect = correctionLidarFrame * tWrong;// pre-multiplying -> successive rotation about a fixed frame
pcl::getTranslationAndEulerAngles (tCorrect, x, y, z, roll, pitch, yaw);
gtsam::Pose3 poseFrom = Pose3(Rot3::RzRyRx(roll, pitch, yaw), Point3(x, y, z));
gtsam::Pose3 poseTo = pclPointTogtsamPose3(copy_cloudKeyPoses6D->points[loopKeyPre]);
gtsam::Vector Vector6(6);
float noiseScore = icp.getFitnessScore();
Vector6 << noiseScore, noiseScore, noiseScore, noiseScore, noiseScore, noiseScore;
noiseModel::Diagonal::shared_ptr constraintNoise = noiseModel::Diagonal::Variances(Vector6);
// Add pose constraint
mtx.lock();
loopIndexQueue.push_back(make_pair(loopKeyCur, loopKeyPre));
loopPoseQueue.push_back(poseFrom.between(poseTo));
loopNoiseQueue.push_back(constraintNoise);
mtx.unlock();
// add loop constriant
loopIndexContainer[loopKeyCur] = loopKeyPre;
}
bool detectLoopClosureDistance(int *latestID, int *closestID)
{
int loopKeyCur = copy_cloudKeyPoses3D->size() - 1;
int loopKeyPre = -1;
// check loop constraint added before
auto it = loopIndexContainer.find(loopKeyCur);
if (it != loopIndexContainer.end())
return false;
// find the closest history key frame
std::vector<int> pointSearchIndLoop;
std::vector<float> pointSearchSqDisLoop;
kdtreeHistoryKeyPoses->setInputCloud(copy_cloudKeyPoses3D);
kdtreeHistoryKeyPoses->radiusSearch(copy_cloudKeyPoses3D->back(), historyKeyframeSearchRadius, pointSearchIndLoop, pointSearchSqDisLoop, 0);
for (int i = 0; i < (int)pointSearchIndLoop.size(); ++i)
{
int id = pointSearchIndLoop[i];
if (abs(copy_cloudKeyPoses6D->points[id].time - timeLaserInfoCur) > historyKeyframeSearchTimeDiff)
{
loopKeyPre = id;
break;
}
}
if (loopKeyPre == -1 || loopKeyCur == loopKeyPre)
return false;
*latestID = loopKeyCur;
*closestID = loopKeyPre;
return true;
}
bool detectLoopClosureExternal(int *latestID, int *closestID)
{
// this function is not used yet, please ignore it
int loopKeyCur = -1;
int loopKeyPre = -1;
std::lock_guard<std::mutex> lock(mtxLoopInfo);
if (loopInfoVec.empty())
return false;
double loopTimeCur = loopInfoVec.front().data[0];
double loopTimePre = loopInfoVec.front().data[1];
loopInfoVec.pop_front();
if (abs(loopTimeCur - loopTimePre) < historyKeyframeSearchTimeDiff)
return false;
int cloudSize = copy_cloudKeyPoses6D->size();
if (cloudSize < 2)
return false;
// latest key
loopKeyCur = cloudSize - 1;
for (int i = cloudSize - 1; i >= 0; --i)
{
if (copy_cloudKeyPoses6D->points[i].time >= loopTimeCur)
loopKeyCur = round(copy_cloudKeyPoses6D->points[i].intensity);
else
break;
}
// previous key
loopKeyPre = 0;
for (int i = 0; i < cloudSize; ++i)
{
if (copy_cloudKeyPoses6D->points[i].time <= loopTimePre)
loopKeyPre = round(copy_cloudKeyPoses6D->points[i].intensity);
else
break;
}
if (loopKeyCur == loopKeyPre)
return false;
auto it = loopIndexContainer.find(loopKeyCur);
if (it != loopIndexContainer.end())
return false;
*latestID = loopKeyCur;
*closestID = loopKeyPre;
return true;
}
void loopFindNearKeyframes(pcl::PointCloud<PointType>::Ptr& nearKeyframes, const int& key, const int& searchNum)
{
// extract near keyframes
nearKeyframes->clear();
int cloudSize = copy_cloudKeyPoses6D->size();
for (int i = -searchNum; i <= searchNum; ++i)
{
int keyNear = key + i;
if (keyNear < 0 || keyNear >= cloudSize )
continue;
*nearKeyframes += *transformPointCloud(cornerCloudKeyFrames[keyNear], ©_cloudKeyPoses6D->points[keyNear]);
*nearKeyframes += *transformPointCloud(surfCloudKeyFrames[keyNear], ©_cloudKeyPoses6D->points[keyNear]);
}
if (nearKeyframes->empty())
return;
// downsample near keyframes
pcl::PointCloud<PointType>::Ptr cloud_temp(new pcl::PointCloud<PointType>());
downSizeFilterICP.setInputCloud(nearKeyframes);
downSizeFilterICP.filter(*cloud_temp);
*nearKeyframes = *cloud_temp;
}
void visualizeLoopClosure()
{
if (loopIndexContainer.empty())
return;
visualization_msgs::msg::MarkerArray markerArray;
// loop nodes
visualization_msgs::msg::Marker markerNode;
markerNode.header.frame_id = odometryFrame;
markerNode.header.stamp = timeLaserInfoStamp;
markerNode.action = visualization_msgs::msg::Marker::ADD;
markerNode.type = visualization_msgs::msg::Marker::SPHERE_LIST;
markerNode.ns = "loop_nodes";
markerNode.id = 0;
markerNode.pose.orientation.w = 1;
markerNode.scale.x = 0.3; markerNode.scale.y = 0.3; markerNode.scale.z = 0.3;
markerNode.color.r = 0; markerNode.color.g = 0.8; markerNode.color.b = 1;
markerNode.color.a = 1;
// loop edges
visualization_msgs::msg::Marker markerEdge;
markerEdge.header.frame_id = odometryFrame;
markerEdge.header.stamp = timeLaserInfoStamp;
markerEdge.action = visualization_msgs::msg::Marker::ADD;
markerEdge.type = visualization_msgs::msg::Marker::LINE_LIST;
markerEdge.ns = "loop_edges";
markerEdge.id = 1;
markerEdge.pose.orientation.w = 1;
markerEdge.scale.x = 0.1;
markerEdge.color.r = 0.9; markerEdge.color.g = 0.9; markerEdge.color.b = 0;
markerEdge.color.a = 1;
for (auto it = loopIndexContainer.begin(); it != loopIndexContainer.end(); ++it)
{
int key_cur = it->first;
int key_pre = it->second;
geometry_msgs::msg::Point p;
p.x = copy_cloudKeyPoses6D->points[key_cur].x;
p.y = copy_cloudKeyPoses6D->points[key_cur].y;
p.z = copy_cloudKeyPoses6D->points[key_cur].z;
markerNode.points.push_back(p);
markerEdge.points.push_back(p);
p.x = copy_cloudKeyPoses6D->points[key_pre].x;
p.y = copy_cloudKeyPoses6D->points[key_pre].y;
p.z = copy_cloudKeyPoses6D->points[key_pre].z;
markerNode.points.push_back(p);
markerEdge.points.push_back(p);
}
markerArray.markers.push_back(markerNode);
markerArray.markers.push_back(markerEdge);
pubLoopConstraintEdge->publish(markerArray);
}
void updateInitialGuess()
{
// save current transformation before any processing
incrementalOdometryAffineFront = trans2Affine3f(transformTobeMapped);
static Eigen::Affine3f lastImuTransformation;
// initialization
if (cloudKeyPoses3D->points.empty())
{
transformTobeMapped[0] = cloudInfo.imu_roll_init;
transformTobeMapped[1] = cloudInfo.imu_pitch_init;
transformTobeMapped[2] = cloudInfo.imu_yaw_init;
if (!useImuHeadingInitialization)
transformTobeMapped[2] = 0;
lastImuTransformation = pcl::getTransformation(0, 0, 0, cloudInfo.imu_roll_init, cloudInfo.imu_pitch_init, cloudInfo.imu_yaw_init); // save imu before return;
return;
}
// use imu pre-integration estimation for pose guess
static bool lastImuPreTransAvailable = false;
static Eigen::Affine3f lastImuPreTransformation;
if (cloudInfo.odom_available == true)
{
Eigen::Affine3f transBack = pcl::getTransformation(
cloudInfo.initial_guess_x, cloudInfo.initial_guess_y, cloudInfo.initial_guess_z,
cloudInfo.initial_guess_roll, cloudInfo.initial_guess_pitch, cloudInfo.initial_guess_yaw);
if (lastImuPreTransAvailable == false)
{
lastImuPreTransformation = transBack;
lastImuPreTransAvailable = true;
} else {
Eigen::Affine3f transIncre = lastImuPreTransformation.inverse() * transBack;
Eigen::Affine3f transTobe = trans2Affine3f(transformTobeMapped);
Eigen::Affine3f transFinal = transTobe * transIncre;
pcl::getTranslationAndEulerAngles(transFinal, transformTobeMapped[3], transformTobeMapped[4], transformTobeMapped[5],
transformTobeMapped[0], transformTobeMapped[1], transformTobeMapped[2]);
lastImuPreTransformation = transBack;
lastImuTransformation = pcl::getTransformation(0, 0, 0, cloudInfo.imu_roll_init, cloudInfo.imu_pitch_init, cloudInfo.imu_yaw_init); // save imu before return;
return;
}
}
// use imu incremental estimation for pose guess (only rotation)
if (cloudInfo.imu_available == true)
{
Eigen::Affine3f transBack = pcl::getTransformation(0, 0, 0, cloudInfo.imu_roll_init, cloudInfo.imu_pitch_init, cloudInfo.imu_yaw_init);
Eigen::Affine3f transIncre = lastImuTransformation.inverse() * transBack;
Eigen::Affine3f transTobe = trans2Affine3f(transformTobeMapped);
Eigen::Affine3f transFinal = transTobe * transIncre;
pcl::getTranslationAndEulerAngles(transFinal, transformTobeMapped[3], transformTobeMapped[4], transformTobeMapped[5],
transformTobeMapped[0], transformTobeMapped[1], transformTobeMapped[2]);
lastImuTransformation = pcl::getTransformation(0, 0, 0, cloudInfo.imu_roll_init, cloudInfo.imu_pitch_init, cloudInfo.imu_yaw_init); // save imu before return;
return;
}
}
void extractForLoopClosure()
{
pcl::PointCloud<PointType>::Ptr cloudToExtract(new pcl::PointCloud<PointType>());
int numPoses = cloudKeyPoses3D->size();
for (int i = numPoses-1; i >= 0; --i)
{
if ((int)cloudToExtract->size() <= surroundingKeyframeSize)
cloudToExtract->push_back(cloudKeyPoses3D->points[i]);
else
break;
}
extractCloud(cloudToExtract);
}
void extractNearby()
{
pcl::PointCloud<PointType>::Ptr surroundingKeyPoses(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr surroundingKeyPosesDS(new pcl::PointCloud<PointType>());
std::vector<int> pointSearchInd;
std::vector<float> pointSearchSqDis;
// extract all the nearby key poses and downsample them
kdtreeSurroundingKeyPoses->setInputCloud(cloudKeyPoses3D); // create kd-tree
kdtreeSurroundingKeyPoses->radiusSearch(cloudKeyPoses3D->back(), (double)surroundingKeyframeSearchRadius, pointSearchInd, pointSearchSqDis);
for (int i = 0; i < (int)pointSearchInd.size(); ++i)
{
int id = pointSearchInd[i];
surroundingKeyPoses->push_back(cloudKeyPoses3D->points[id]);
}
downSizeFilterSurroundingKeyPoses.setInputCloud(surroundingKeyPoses);
downSizeFilterSurroundingKeyPoses.filter(*surroundingKeyPosesDS);
for(auto& pt : surroundingKeyPosesDS->points)
{
kdtreeSurroundingKeyPoses->nearestKSearch(pt, 1, pointSearchInd, pointSearchSqDis);
pt.intensity = cloudKeyPoses3D->points[pointSearchInd[0]].intensity;
}
// also extract some latest key frames in case the robot rotates in one position
int numPoses = cloudKeyPoses3D->size();
for (int i = numPoses-1; i >= 0; --i)
{
if (timeLaserInfoCur - cloudKeyPoses6D->points[i].time < 10.0)
surroundingKeyPosesDS->push_back(cloudKeyPoses3D->points[i]);
else
break;
}
extractCloud(surroundingKeyPosesDS);
}
void extractCloud(pcl::PointCloud<PointType>::Ptr cloudToExtract)
{
// fuse the map
laserCloudCornerFromMap->clear();
laserCloudSurfFromMap->clear();
for (int i = 0; i < (int)cloudToExtract->size(); ++i)
{
if (pointDistance(cloudToExtract->points[i], cloudKeyPoses3D->back()) > surroundingKeyframeSearchRadius)
continue;
int thisKeyInd = (int)cloudToExtract->points[i].intensity;
if (laserCloudMapContainer.find(thisKeyInd) != laserCloudMapContainer.end())
{
// transformed cloud available
*laserCloudCornerFromMap += laserCloudMapContainer[thisKeyInd].first;
*laserCloudSurfFromMap += laserCloudMapContainer[thisKeyInd].second;
} else {
// transformed cloud not available
pcl::PointCloud<PointType> laserCloudCornerTemp = *transformPointCloud(cornerCloudKeyFrames[thisKeyInd], &cloudKeyPoses6D->points[thisKeyInd]);
pcl::PointCloud<PointType> laserCloudSurfTemp = *transformPointCloud(surfCloudKeyFrames[thisKeyInd], &cloudKeyPoses6D->points[thisKeyInd]);
*laserCloudCornerFromMap += laserCloudCornerTemp;
*laserCloudSurfFromMap += laserCloudSurfTemp;
laserCloudMapContainer[thisKeyInd] = make_pair(laserCloudCornerTemp, laserCloudSurfTemp);
}
}
// Downsample the surrounding corner key frames (or map)
downSizeFilterCorner.setInputCloud(laserCloudCornerFromMap);
downSizeFilterCorner.filter(*laserCloudCornerFromMapDS);
laserCloudCornerFromMapDSNum = laserCloudCornerFromMapDS->size();
// Downsample the surrounding surf key frames (or map)
downSizeFilterSurf.setInputCloud(laserCloudSurfFromMap);
downSizeFilterSurf.filter(*laserCloudSurfFromMapDS);
laserCloudSurfFromMapDSNum = laserCloudSurfFromMapDS->size();
// clear map cache if too large
if (laserCloudMapContainer.size() > 1000)
laserCloudMapContainer.clear();
}
void extractSurroundingKeyFrames()
{
if (cloudKeyPoses3D->points.empty() == true)
return;
// if (loopClosureEnableFlag == true)
// {
// extractForLoopClosure();
// } else {
// extractNearby();
// }
extractNearby();
}
void downsampleCurrentScan()
{
// Downsample cloud from current scan
laserCloudCornerLastDS->clear();
downSizeFilterCorner.setInputCloud(laserCloudCornerLast);
downSizeFilterCorner.filter(*laserCloudCornerLastDS);
laserCloudCornerLastDSNum = laserCloudCornerLastDS->size();
laserCloudSurfLastDS->clear();
downSizeFilterSurf.setInputCloud(laserCloudSurfLast);
downSizeFilterSurf.filter(*laserCloudSurfLastDS);
laserCloudSurfLastDSNum = laserCloudSurfLastDS->size();
}
void updatePointAssociateToMap()
{
transPointAssociateToMap = trans2Affine3f(transformTobeMapped);
}
void cornerOptimization()
{
updatePointAssociateToMap();
#pragma omp parallel for num_threads(numberOfCores)
for (int i = 0; i < laserCloudCornerLastDSNum; i++)
{
PointType pointOri, pointSel, coeff;
std::vector<int> pointSearchInd;
std::vector<float> pointSearchSqDis;
pointOri = laserCloudCornerLastDS->points[i];
pointAssociateToMap(&pointOri, &pointSel);
kdtreeCornerFromMap->nearestKSearch(pointSel, 5, pointSearchInd, pointSearchSqDis);
cv::Mat matA1(3, 3, CV_32F, cv::Scalar::all(0));
cv::Mat matD1(1, 3, CV_32F, cv::Scalar::all(0));
cv::Mat matV1(3, 3, CV_32F, cv::Scalar::all(0));
if (pointSearchSqDis[4] < 1.0) {
float cx = 0, cy = 0, cz = 0;
for (int j = 0; j < 5; j++) {
cx += laserCloudCornerFromMapDS->points[pointSearchInd[j]].x;
cy += laserCloudCornerFromMapDS->points[pointSearchInd[j]].y;
cz += laserCloudCornerFromMapDS->points[pointSearchInd[j]].z;
}
cx /= 5; cy /= 5; cz /= 5;
float a11 = 0, a12 = 0, a13 = 0, a22 = 0, a23 = 0, a33 = 0;
for (int j = 0; j < 5; j++) {
float ax = laserCloudCornerFromMapDS->points[pointSearchInd[j]].x - cx;
float ay = laserCloudCornerFromMapDS->points[pointSearchInd[j]].y - cy;
float az = laserCloudCornerFromMapDS->points[pointSearchInd[j]].z - cz;
a11 += ax * ax; a12 += ax * ay; a13 += ax * az;
a22 += ay * ay; a23 += ay * az;
a33 += az * az;
}
a11 /= 5; a12 /= 5; a13 /= 5; a22 /= 5; a23 /= 5; a33 /= 5;
matA1.at<float>(0, 0) = a11; matA1.at<float>(0, 1) = a12; matA1.at<float>(0, 2) = a13;
matA1.at<float>(1, 0) = a12; matA1.at<float>(1, 1) = a22; matA1.at<float>(1, 2) = a23;
matA1.at<float>(2, 0) = a13; matA1.at<float>(2, 1) = a23; matA1.at<float>(2, 2) = a33;
cv::eigen(matA1, matD1, matV1);
if (matD1.at<float>(0, 0) > 3 * matD1.at<float>(0, 1)) {
float x0 = pointSel.x;
float y0 = pointSel.y;
float z0 = pointSel.z;
float x1 = cx + 0.1 * matV1.at<float>(0, 0);
float y1 = cy + 0.1 * matV1.at<float>(0, 1);
float z1 = cz + 0.1 * matV1.at<float>(0, 2);
float x2 = cx - 0.1 * matV1.at<float>(0, 0);
float y2 = cy - 0.1 * matV1.at<float>(0, 1);
float z2 = cz - 0.1 * matV1.at<float>(0, 2);
float a012 = sqrt(((x0 - x1)*(y0 - y2) - (x0 - x2)*(y0 - y1)) * ((x0 - x1)*(y0 - y2) - (x0 - x2)*(y0 - y1))
+ ((x0 - x1)*(z0 - z2) - (x0 - x2)*(z0 - z1)) * ((x0 - x1)*(z0 - z2) - (x0 - x2)*(z0 - z1))
+ ((y0 - y1)*(z0 - z2) - (y0 - y2)*(z0 - z1)) * ((y0 - y1)*(z0 - z2) - (y0 - y2)*(z0 - z1)));
float l12 = sqrt((x1 - x2)*(x1 - x2) + (y1 - y2)*(y1 - y2) + (z1 - z2)*(z1 - z2));
float la = ((y1 - y2)*((x0 - x1)*(y0 - y2) - (x0 - x2)*(y0 - y1))
+ (z1 - z2)*((x0 - x1)*(z0 - z2) - (x0 - x2)*(z0 - z1))) / a012 / l12;
float lb = -((x1 - x2)*((x0 - x1)*(y0 - y2) - (x0 - x2)*(y0 - y1))
- (z1 - z2)*((y0 - y1)*(z0 - z2) - (y0 - y2)*(z0 - z1))) / a012 / l12;
float lc = -((x1 - x2)*((x0 - x1)*(z0 - z2) - (x0 - x2)*(z0 - z1))
+ (y1 - y2)*((y0 - y1)*(z0 - z2) - (y0 - y2)*(z0 - z1))) / a012 / l12;
float ld2 = a012 / l12;
float s = 1 - 0.9 * fabs(ld2);
coeff.x = s * la;
coeff.y = s * lb;
coeff.z = s * lc;
coeff.intensity = s * ld2;
if (s > 0.1) {
laserCloudOriCornerVec[i] = pointOri;
coeffSelCornerVec[i] = coeff;
laserCloudOriCornerFlag[i] = true;
}
}
}
}
}
void surfOptimization()
{
updatePointAssociateToMap();
#pragma omp parallel for num_threads(numberOfCores)
for (int i = 0; i < laserCloudSurfLastDSNum; i++)
{
PointType pointOri, pointSel, coeff;
std::vector<int> pointSearchInd;
std::vector<float> pointSearchSqDis;
pointOri = laserCloudSurfLastDS->points[i];
pointAssociateToMap(&pointOri, &pointSel);
kdtreeSurfFromMap->nearestKSearch(pointSel, 5, pointSearchInd, pointSearchSqDis);
Eigen::Matrix<float, 5, 3> matA0;
Eigen::Matrix<float, 5, 1> matB0;
Eigen::Vector3f matX0;
matA0.setZero();
matB0.fill(-1);
matX0.setZero();
if (pointSearchSqDis[4] < 1.0) {
for (int j = 0; j < 5; j++) {
matA0(j, 0) = laserCloudSurfFromMapDS->points[pointSearchInd[j]].x;
matA0(j, 1) = laserCloudSurfFromMapDS->points[pointSearchInd[j]].y;
matA0(j, 2) = laserCloudSurfFromMapDS->points[pointSearchInd[j]].z;
}
matX0 = matA0.colPivHouseholderQr().solve(matB0);
float pa = matX0(0, 0);
float pb = matX0(1, 0);
float pc = matX0(2, 0);
float pd = 1;
float ps = sqrt(pa * pa + pb * pb + pc * pc);
pa /= ps; pb /= ps; pc /= ps; pd /= ps;
bool planeValid = true;
for (int j = 0; j < 5; j++) {
if (fabs(pa * laserCloudSurfFromMapDS->points[pointSearchInd[j]].x +
pb * laserCloudSurfFromMapDS->points[pointSearchInd[j]].y +
pc * laserCloudSurfFromMapDS->points[pointSearchInd[j]].z + pd) > 0.2) {
planeValid = false;
break;
}
}
if (planeValid) {
float pd2 = pa * pointSel.x + pb * pointSel.y + pc * pointSel.z + pd;
float s = 1 - 0.9 * fabs(pd2) / sqrt(sqrt(pointOri.x * pointOri.x
+ pointOri.y * pointOri.y + pointOri.z * pointOri.z));
coeff.x = s * pa;
coeff.y = s * pb;
coeff.z = s * pc;
coeff.intensity = s * pd2;
if (s > 0.1) {
laserCloudOriSurfVec[i] = pointOri;
coeffSelSurfVec[i] = coeff;
laserCloudOriSurfFlag[i] = true;
}
}
}
}
}
void combineOptimizationCoeffs()
{
// combine corner coeffs
for (int i = 0; i < laserCloudCornerLastDSNum; ++i){
if (laserCloudOriCornerFlag[i] == true){
laserCloudOri->push_back(laserCloudOriCornerVec[i]);
coeffSel->push_back(coeffSelCornerVec[i]);
}
}
// combine surf coeffs
for (int i = 0; i < laserCloudSurfLastDSNum; ++i){
if (laserCloudOriSurfFlag[i] == true){
laserCloudOri->push_back(laserCloudOriSurfVec[i]);
coeffSel->push_back(coeffSelSurfVec[i]);
}
}
// reset flag for next iteration
std::fill(laserCloudOriCornerFlag.begin(), laserCloudOriCornerFlag.end(), false);
std::fill(laserCloudOriSurfFlag.begin(), laserCloudOriSurfFlag.end(), false);
}
bool LMOptimization(int iterCount)
{
// This optimization is from the original loam_velodyne by Ji Zhang, need to cope with coordinate transformation
// lidar <- camera --- camera <- lidar
// x = z --- x = y
// y = x --- y = z
// z = y --- z = x
// roll = yaw --- roll = pitch
// pitch = roll --- pitch = yaw
// yaw = pitch --- yaw = roll
// lidar -> camera
float srx = sin(transformTobeMapped[1]);
float crx = cos(transformTobeMapped[1]);
float sry = sin(transformTobeMapped[2]);
float cry = cos(transformTobeMapped[2]);
float srz = sin(transformTobeMapped[0]);
float crz = cos(transformTobeMapped[0]);
int laserCloudSelNum = laserCloudOri->size();
if (laserCloudSelNum < 50) {
return false;
}
cv::Mat matA(laserCloudSelNum, 6, CV_32F, cv::Scalar::all(0));
cv::Mat matAt(6, laserCloudSelNum, CV_32F, cv::Scalar::all(0));
cv::Mat matAtA(6, 6, CV_32F, cv::Scalar::all(0));
cv::Mat matB(laserCloudSelNum, 1, CV_32F, cv::Scalar::all(0));
cv::Mat matAtB(6, 1, CV_32F, cv::Scalar::all(0));
cv::Mat matX(6, 1, CV_32F, cv::Scalar::all(0));
cv::Mat matP(6, 6, CV_32F, cv::Scalar::all(0));
PointType pointOri, coeff;
for (int i = 0; i < laserCloudSelNum; i++) {
// lidar -> camera
pointOri.x = laserCloudOri->points[i].y;
pointOri.y = laserCloudOri->points[i].z;
pointOri.z = laserCloudOri->points[i].x;
// lidar -> camera
coeff.x = coeffSel->points[i].y;
coeff.y = coeffSel->points[i].z;
coeff.z = coeffSel->points[i].x;
coeff.intensity = coeffSel->points[i].intensity;
// in camera
float arx = (crx*sry*srz*pointOri.x + crx*crz*sry*pointOri.y - srx*sry*pointOri.z) * coeff.x
+ (-srx*srz*pointOri.x - crz*srx*pointOri.y - crx*pointOri.z) * coeff.y
+ (crx*cry*srz*pointOri.x + crx*cry*crz*pointOri.y - cry*srx*pointOri.z) * coeff.z;
float ary = ((cry*srx*srz - crz*sry)*pointOri.x
+ (sry*srz + cry*crz*srx)*pointOri.y + crx*cry*pointOri.z) * coeff.x
+ ((-cry*crz - srx*sry*srz)*pointOri.x
+ (cry*srz - crz*srx*sry)*pointOri.y - crx*sry*pointOri.z) * coeff.z;
float arz = ((crz*srx*sry - cry*srz)*pointOri.x + (-cry*crz-srx*sry*srz)*pointOri.y)*coeff.x
+ (crx*crz*pointOri.x - crx*srz*pointOri.y) * coeff.y
+ ((sry*srz + cry*crz*srx)*pointOri.x + (crz*sry-cry*srx*srz)*pointOri.y)*coeff.z;
// lidar -> camera
matA.at<float>(i, 0) = arz;
matA.at<float>(i, 1) = arx;
matA.at<float>(i, 2) = ary;
matA.at<float>(i, 3) = coeff.z;
matA.at<float>(i, 4) = coeff.x;
matA.at<float>(i, 5) = coeff.y;
matB.at<float>(i, 0) = -coeff.intensity;
}
cv::transpose(matA, matAt);
matAtA = matAt * matA;
matAtB = matAt * matB;
cv::solve(matAtA, matAtB, matX, cv::DECOMP_QR);
if (iterCount == 0) {
cv::Mat matE(1, 6, CV_32F, cv::Scalar::all(0));
cv::Mat matV(6, 6, CV_32F, cv::Scalar::all(0));
cv::Mat matV2(6, 6, CV_32F, cv::Scalar::all(0));
cv::eigen(matAtA, matE, matV);
matV.copyTo(matV2);
isDegenerate = false;
float eignThre[6] = {100, 100, 100, 100, 100, 100};
for (int i = 5; i >= 0; i--) {
if (matE.at<float>(0, i) < eignThre[i]) {
for (int j = 0; j < 6; j++) {
matV2.at<float>(i, j) = 0;
}
isDegenerate = true;
} else {
break;
}
}
matP = matV.inv() * matV2;
}
if (isDegenerate)
{
cv::Mat matX2(6, 1, CV_32F, cv::Scalar::all(0));
matX.copyTo(matX2);
matX = matP * matX2;
}
transformTobeMapped[0] += matX.at<float>(0, 0);
transformTobeMapped[1] += matX.at<float>(1, 0);
transformTobeMapped[2] += matX.at<float>(2, 0);
transformTobeMapped[3] += matX.at<float>(3, 0);
transformTobeMapped[4] += matX.at<float>(4, 0);
transformTobeMapped[5] += matX.at<float>(5, 0);
float deltaR = sqrt(
pow(pcl::rad2deg(matX.at<float>(0, 0)), 2) +
pow(pcl::rad2deg(matX.at<float>(1, 0)), 2) +
pow(pcl::rad2deg(matX.at<float>(2, 0)), 2));
float deltaT = sqrt(
pow(matX.at<float>(3, 0) * 100, 2) +
pow(matX.at<float>(4, 0) * 100, 2) +
pow(matX.at<float>(5, 0) * 100, 2));
if (deltaR < 0.05 && deltaT < 0.05) {
return true; // converged
}
return false; // keep optimizing
}
void scan2MapOptimization()
{
if (cloudKeyPoses3D->points.empty())
return;
if (laserCloudCornerLastDSNum > edgeFeatureMinValidNum && laserCloudSurfLastDSNum > surfFeatureMinValidNum)
{
kdtreeCornerFromMap->setInputCloud(laserCloudCornerFromMapDS);
kdtreeSurfFromMap->setInputCloud(laserCloudSurfFromMapDS);
for (int iterCount = 0; iterCount < 30; iterCount++)
{
laserCloudOri->clear();
coeffSel->clear();
cornerOptimization();
surfOptimization();
combineOptimizationCoeffs();
if (LMOptimization(iterCount) == true)
break;
}
transformUpdate();
} else {
RCLCPP_WARN(get_logger(), "Not enough features! Only %d edge and %d planar features available.", laserCloudCornerLastDSNum, laserCloudSurfLastDSNum);
}
}
void transformUpdate()
{
if (cloudInfo.imu_available == true)
{
if (std::abs(cloudInfo.imu_pitch_init) < 1.4)
{
double imuWeight = imuRPYWeight;
tf2::Quaternion imuQuaternion;
tf2::Quaternion transformQuaternion;
double rollMid, pitchMid, yawMid;
// slerp roll
transformQuaternion.setRPY(transformTobeMapped[0], 0, 0);
imuQuaternion.setRPY(cloudInfo.imu_roll_init, 0, 0);
tf2::Matrix3x3(transformQuaternion.slerp(imuQuaternion, imuWeight)).getRPY(rollMid, pitchMid, yawMid);
transformTobeMapped[0] = rollMid;
// slerp pitch
transformQuaternion.setRPY(0, transformTobeMapped[1], 0);
imuQuaternion.setRPY(0, cloudInfo.imu_pitch_init, 0);
tf2::Matrix3x3(transformQuaternion.slerp(imuQuaternion, imuWeight)).getRPY(rollMid, pitchMid, yawMid);
transformTobeMapped[1] = pitchMid;
}
}
transformTobeMapped[0] = constraintTransformation(transformTobeMapped[0], rotation_tollerance);
transformTobeMapped[1] = constraintTransformation(transformTobeMapped[1], rotation_tollerance);
transformTobeMapped[5] = constraintTransformation(transformTobeMapped[5], z_tollerance);
incrementalOdometryAffineBack = trans2Affine3f(transformTobeMapped);
}
float constraintTransformation(float value, float limit)
{
if (value < -limit)
value = -limit;
if (value > limit)
value = limit;
return value;
}
bool saveFrame()
{
if (cloudKeyPoses3D->points.empty())
return true;
if (sensor == SensorType::LIVOX)
{
if (timeLaserInfoCur - cloudKeyPoses6D->back().time > 1.0)
return true;
}
Eigen::Affine3f transStart = pclPointToAffine3f(cloudKeyPoses6D->back());
Eigen::Affine3f transFinal = pcl::getTransformation(transformTobeMapped[3], transformTobeMapped[4], transformTobeMapped[5],
transformTobeMapped[0], transformTobeMapped[1], transformTobeMapped[2]);
Eigen::Affine3f transBetween = transStart.inverse() * transFinal;
float x, y, z, roll, pitch, yaw;
pcl::getTranslationAndEulerAngles(transBetween, x, y, z, roll, pitch, yaw);
if (abs(roll) < surroundingkeyframeAddingAngleThreshold &&
abs(pitch) < surroundingkeyframeAddingAngleThreshold &&
abs(yaw) < surroundingkeyframeAddingAngleThreshold &&
sqrt(x*x + y*y + z*z) < surroundingkeyframeAddingDistThreshold)
return false;
return true;
}
void addOdomFactor()
{
if (cloudKeyPoses3D->points.empty())
{
noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Variances((Vector(6) << 1e-2, 1e-2, M_PI*M_PI, 1e8, 1e8, 1e8).finished()); // rad*rad, meter*meter
gtSAMgraph.add(PriorFactor<Pose3>(0, trans2gtsamPose(transformTobeMapped), priorNoise));
initialEstimate.insert(0, trans2gtsamPose(transformTobeMapped));
}else{
noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Variances((Vector(6) << 1e-6, 1e-6, 1e-6, 1e-4, 1e-4, 1e-4).finished());
gtsam::Pose3 poseFrom = pclPointTogtsamPose3(cloudKeyPoses6D->points.back());
gtsam::Pose3 poseTo = trans2gtsamPose(transformTobeMapped);
gtSAMgraph.add(BetweenFactor<Pose3>(cloudKeyPoses3D->size()-1, cloudKeyPoses3D->size(), poseFrom.between(poseTo), odometryNoise));
initialEstimate.insert(cloudKeyPoses3D->size(), poseTo);
}
}
void addGPSFactor()
{
if (gpsQueue.empty())
return;
// wait for system initialized and settles down
if (cloudKeyPoses3D->points.empty())
return;
else
{
if (pointDistance(cloudKeyPoses3D->front(), cloudKeyPoses3D->back()) < 5.0)
return;
}
// pose covariance small, no need to correct
if (poseCovariance(3,3) < poseCovThreshold && poseCovariance(4,4) < poseCovThreshold)
return;
// last gps position
static PointType lastGPSPoint;
while (!gpsQueue.empty())
{
if (stamp2Sec(gpsQueue.front().header.stamp) < timeLaserInfoCur - 0.2)
{
// message too old
gpsQueue.pop_front();
}
else if (stamp2Sec(gpsQueue.front().header.stamp) > timeLaserInfoCur + 0.2)
{
// message too new
break;
}
else
{
nav_msgs::msg::Odometry thisGPS = gpsQueue.front();
gpsQueue.pop_front();
// GPS too noisy, skip
float noise_x = thisGPS.pose.covariance[0];
float noise_y = thisGPS.pose.covariance[7];
float noise_z = thisGPS.pose.covariance[14];
if (noise_x > gpsCovThreshold || noise_y > gpsCovThreshold)
continue;
float gps_x = thisGPS.pose.pose.position.x;
float gps_y = thisGPS.pose.pose.position.y;
float gps_z = thisGPS.pose.pose.position.z;
if (!useGpsElevation)
{
gps_z = transformTobeMapped[5];
noise_z = 0.01;
}
// GPS not properly initialized (0,0,0)
if (abs(gps_x) < 1e-6 && abs(gps_y) < 1e-6)
continue;
// Add GPS every a few meters
PointType curGPSPoint;
curGPSPoint.x = gps_x;
curGPSPoint.y = gps_y;
curGPSPoint.z = gps_z;
if (pointDistance(curGPSPoint, lastGPSPoint) < 5.0)
continue;
else
lastGPSPoint = curGPSPoint;
gtsam::Vector Vector3(3);
Vector3 << max(noise_x, 1.0f), max(noise_y, 1.0f), max(noise_z, 1.0f);
noiseModel::Diagonal::shared_ptr gps_noise = noiseModel::Diagonal::Variances(Vector3);
gtsam::GPSFactor gps_factor(cloudKeyPoses3D->size(), gtsam::Point3(gps_x, gps_y, gps_z), gps_noise);
gtSAMgraph.add(gps_factor);
aLoopIsClosed = true;
break;
}
}
}
void addLoopFactor()
{
if (loopIndexQueue.empty())
return;
for (int i = 0; i < (int)loopIndexQueue.size(); ++i)
{
int indexFrom = loopIndexQueue[i].first;
int indexTo = loopIndexQueue[i].second;
gtsam::Pose3 poseBetween = loopPoseQueue[i];
gtsam::noiseModel::Diagonal::shared_ptr noiseBetween = loopNoiseQueue[i];
gtSAMgraph.add(BetweenFactor<Pose3>(indexFrom, indexTo, poseBetween, noiseBetween));
}
loopIndexQueue.clear();
loopPoseQueue.clear();
loopNoiseQueue.clear();
aLoopIsClosed = true;
}
void saveKeyFramesAndFactor()
{
if (saveFrame() == false)
return;
// odom factor
addOdomFactor();
// gps factor
addGPSFactor();
// loop factor
addLoopFactor();
// cout << "****************************************************" << endl;
// gtSAMgraph.print("GTSAM Graph:\n");
// update iSAM
isam->update(gtSAMgraph, initialEstimate);
isam->update();
if (aLoopIsClosed == true)
{
isam->update();
isam->update();
isam->update();
isam->update();
isam->update();
}
gtSAMgraph.resize(0);
initialEstimate.clear();
//save key poses
PointType thisPose3D;
PointTypePose thisPose6D;
Pose3 latestEstimate;
isamCurrentEstimate = isam->calculateEstimate();
latestEstimate = isamCurrentEstimate.at<Pose3>(isamCurrentEstimate.size()-1);
// cout << "****************************************************" << endl;
// isamCurrentEstimate.print("Current estimate: ");
thisPose3D.x = latestEstimate.translation().x();
thisPose3D.y = latestEstimate.translation().y();
thisPose3D.z = latestEstimate.translation().z();
thisPose3D.intensity = cloudKeyPoses3D->size(); // this can be used as index
cloudKeyPoses3D->push_back(thisPose3D);
thisPose6D.x = thisPose3D.x;
thisPose6D.y = thisPose3D.y;
thisPose6D.z = thisPose3D.z;
thisPose6D.intensity = thisPose3D.intensity ; // this can be used as index
thisPose6D.roll = latestEstimate.rotation().roll();
thisPose6D.pitch = latestEstimate.rotation().pitch();
thisPose6D.yaw = latestEstimate.rotation().yaw();
thisPose6D.time = timeLaserInfoCur;
cloudKeyPoses6D->push_back(thisPose6D);
// cout << "****************************************************" << endl;
// cout << "Pose covariance:" << endl;
// cout << isam->marginalCovariance(isamCurrentEstimate.size()-1) << endl << endl;
poseCovariance = isam->marginalCovariance(isamCurrentEstimate.size()-1);
// save updated transform
transformTobeMapped[0] = latestEstimate.rotation().roll();
transformTobeMapped[1] = latestEstimate.rotation().pitch();
transformTobeMapped[2] = latestEstimate.rotation().yaw();
transformTobeMapped[3] = latestEstimate.translation().x();
transformTobeMapped[4] = latestEstimate.translation().y();
transformTobeMapped[5] = latestEstimate.translation().z();
// save all the received edge and surf points
pcl::PointCloud<PointType>::Ptr thisCornerKeyFrame(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr thisSurfKeyFrame(new pcl::PointCloud<PointType>());
pcl::copyPointCloud(*laserCloudCornerLastDS, *thisCornerKeyFrame);
pcl::copyPointCloud(*laserCloudSurfLastDS, *thisSurfKeyFrame);
// save key frame cloud
cornerCloudKeyFrames.push_back(thisCornerKeyFrame);
surfCloudKeyFrames.push_back(thisSurfKeyFrame);
// save path for visualization
updatePath(thisPose6D);
}
void correctPoses()
{
if (cloudKeyPoses3D->points.empty())
return;
if (aLoopIsClosed == true)
{
// clear map cache
laserCloudMapContainer.clear();
// clear path
globalPath.poses.clear();
// update key poses
int numPoses = isamCurrentEstimate.size();
for (int i = 0; i < numPoses; ++i)
{
cloudKeyPoses3D->points[i].x = isamCurrentEstimate.at<Pose3>(i).translation().x();
cloudKeyPoses3D->points[i].y = isamCurrentEstimate.at<Pose3>(i).translation().y();
cloudKeyPoses3D->points[i].z = isamCurrentEstimate.at<Pose3>(i).translation().z();
cloudKeyPoses6D->points[i].x = cloudKeyPoses3D->points[i].x;
cloudKeyPoses6D->points[i].y = cloudKeyPoses3D->points[i].y;
cloudKeyPoses6D->points[i].z = cloudKeyPoses3D->points[i].z;
cloudKeyPoses6D->points[i].roll = isamCurrentEstimate.at<Pose3>(i).rotation().roll();
cloudKeyPoses6D->points[i].pitch = isamCurrentEstimate.at<Pose3>(i).rotation().pitch();
cloudKeyPoses6D->points[i].yaw = isamCurrentEstimate.at<Pose3>(i).rotation().yaw();
updatePath(cloudKeyPoses6D->points[i]);
}
aLoopIsClosed = false;
}
}
void updatePath(const PointTypePose& pose_in)
{
geometry_msgs::msg::PoseStamped pose_stamped;
pose_stamped.header.stamp = rclcpp::Time(pose_in.time * 1e9);
pose_stamped.header.frame_id = odometryFrame;
pose_stamped.pose.position.x = pose_in.x;
pose_stamped.pose.position.y = pose_in.y;
pose_stamped.pose.position.z = pose_in.z;
tf2::Quaternion q;
q.setRPY(pose_in.roll, pose_in.pitch, pose_in.yaw);
pose_stamped.pose.orientation.x = q.x();
pose_stamped.pose.orientation.y = q.y();
pose_stamped.pose.orientation.z = q.z();
pose_stamped.pose.orientation.w = q.w();
globalPath.poses.push_back(pose_stamped);
}
void publishOdometry()
{
// Publish odometry for ROS (global)
nav_msgs::msg::Odometry laserOdometryROS;
laserOdometryROS.header.stamp = timeLaserInfoStamp;
laserOdometryROS.header.frame_id = odometryFrame;
laserOdometryROS.child_frame_id = "odom_mapping";
laserOdometryROS.pose.pose.position.x = transformTobeMapped[3];
laserOdometryROS.pose.pose.position.y = transformTobeMapped[4];
laserOdometryROS.pose.pose.position.z = transformTobeMapped[5];
tf2::Quaternion quat_tf;
quat_tf.setRPY(transformTobeMapped[0], transformTobeMapped[1], transformTobeMapped[2]);
geometry_msgs::msg::Quaternion quat_msg;
tf2::convert(quat_tf, quat_msg);
laserOdometryROS.pose.pose.orientation = quat_msg;
pubLaserOdometryGlobal->publish(laserOdometryROS);
// Publish TF
quat_tf.setRPY(transformTobeMapped[0], transformTobeMapped[1], transformTobeMapped[2]);
tf2::Transform t_odom_to_lidar = tf2::Transform(quat_tf, tf2::Vector3(transformTobeMapped[3], transformTobeMapped[4], transformTobeMapped[5]));
tf2::TimePoint time_point = tf2_ros::fromRclcpp(timeLaserInfoStamp);
tf2::Stamped<tf2::Transform> temp_odom_to_lidar(t_odom_to_lidar, time_point, odometryFrame);
geometry_msgs::msg::TransformStamped trans_odom_to_lidar;
tf2::convert(temp_odom_to_lidar, trans_odom_to_lidar);
trans_odom_to_lidar.child_frame_id = "lidar_link";
br->sendTransform(trans_odom_to_lidar);
// Publish odometry for ROS (incremental)
static bool lastIncreOdomPubFlag = false;
static nav_msgs::msg::Odometry laserOdomIncremental; // incremental odometry msg
static Eigen::Affine3f increOdomAffine; // incremental odometry in affine
if (lastIncreOdomPubFlag == false)
{
lastIncreOdomPubFlag = true;
laserOdomIncremental = laserOdometryROS;
increOdomAffine = trans2Affine3f(transformTobeMapped);
} else {
Eigen::Affine3f affineIncre = incrementalOdometryAffineFront.inverse() * incrementalOdometryAffineBack;
increOdomAffine = increOdomAffine * affineIncre;
float x, y, z, roll, pitch, yaw;
pcl::getTranslationAndEulerAngles (increOdomAffine, x, y, z, roll, pitch, yaw);
if (cloudInfo.imu_available == true)
{
if (std::abs(cloudInfo.imu_pitch_init) < 1.4)
{
double imuWeight = 0.1;
tf2::Quaternion imuQuaternion;
tf2::Quaternion transformQuaternion;
double rollMid, pitchMid, yawMid;
// slerp roll
transformQuaternion.setRPY(roll, 0, 0);
imuQuaternion.setRPY(cloudInfo.imu_roll_init, 0, 0);
tf2::Matrix3x3(transformQuaternion.slerp(imuQuaternion, imuWeight)).getRPY(rollMid, pitchMid, yawMid);
roll = rollMid;
// slerp pitch
transformQuaternion.setRPY(0, pitch, 0);
imuQuaternion.setRPY(0, cloudInfo.imu_pitch_init, 0);
tf2::Matrix3x3(transformQuaternion.slerp(imuQuaternion, imuWeight)).getRPY(rollMid, pitchMid, yawMid);
pitch = pitchMid;
}
}
laserOdomIncremental.header.stamp = timeLaserInfoStamp;
laserOdomIncremental.header.frame_id = odometryFrame;
laserOdomIncremental.child_frame_id = "odom_mapping";
laserOdomIncremental.pose.pose.position.x = x;
laserOdomIncremental.pose.pose.position.y = y;
laserOdomIncremental.pose.pose.position.z = z;
tf2::Quaternion quat_tf;
quat_tf.setRPY(roll, pitch, yaw);
geometry_msgs::msg::Quaternion quat_msg;
tf2::convert(quat_tf, quat_msg);
laserOdomIncremental.pose.pose.orientation = quat_msg;
if (isDegenerate)
laserOdomIncremental.pose.covariance[0] = 1;
else
laserOdomIncremental.pose.covariance[0] = 0;
}
pubLaserOdometryIncremental->publish(laserOdomIncremental);
}
void publishFrames()
{
if (cloudKeyPoses3D->points.empty())
return;
// publish key poses
publishCloud(pubKeyPoses, cloudKeyPoses3D, timeLaserInfoStamp, odometryFrame);
// Publish surrounding key frames
publishCloud(pubRecentKeyFrames, laserCloudSurfFromMapDS, timeLaserInfoStamp, odometryFrame);
// publish registered key frame
if (pubRecentKeyFrame->get_subscription_count() != 0)
{
pcl::PointCloud<PointType>::Ptr cloudOut(new pcl::PointCloud<PointType>());
PointTypePose thisPose6D = trans2PointTypePose(transformTobeMapped);
*cloudOut += *transformPointCloud(laserCloudCornerLastDS, &thisPose6D);
*cloudOut += *transformPointCloud(laserCloudSurfLastDS, &thisPose6D);
publishCloud(pubRecentKeyFrame, cloudOut, timeLaserInfoStamp, odometryFrame);
}
// publish registered high-res raw cloud
if (pubCloudRegisteredRaw->get_subscription_count() != 0)
{
pcl::PointCloud<PointType>::Ptr cloudOut(new pcl::PointCloud<PointType>());
pcl::fromROSMsg(cloudInfo.cloud_deskewed, *cloudOut);
PointTypePose thisPose6D = trans2PointTypePose(transformTobeMapped);
*cloudOut = *transformPointCloud(cloudOut, &thisPose6D);
publishCloud(pubCloudRegisteredRaw, cloudOut, timeLaserInfoStamp, odometryFrame);
}
// publish path
if (pubPath->get_subscription_count() != 0)
{
globalPath.header.stamp = timeLaserInfoStamp;
globalPath.header.frame_id = odometryFrame;
pubPath->publish(globalPath);
}
}
};
int main(int argc, char** argv)
{
rclcpp::init(argc, argv);
rclcpp::NodeOptions options;
options.use_intra_process_comms(true);
rclcpp::executors::SingleThreadedExecutor exec;
auto MO = std::make_shared<mapOptimization>(options);
exec.add_node(MO);
RCLCPP_INFO(rclcpp::get_logger("rclcpp"), "\033[1;32m----> Map Optimization Started.\033[0m");
std::thread loopthread(&mapOptimization::loopClosureThread, MO);
std::thread visualizeMapThread(&mapOptimization::visualizeGlobalMapThread, MO);
exec.spin();
rclcpp::shutdown();
loopthread.join();
visualizeMapThread.join();
// 确保保存地图线程完成
if (MO->saveMapThread.joinable()) {
MO->saveMapThread.join();
}
return 0;
}这样就可以输入指令保存地图了
现在数据集就跑完了，后续还会更新实际的哈，可能要过一阵子就是
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