三维目标检测：（二）用ROS获取传感器(Kinect DK)点云并滤波

• [ 1] 需要配置Clion

https://blog.csdn.net/weixin_38593194/article/details/85122716

• [ 2] 创建ros工作空间和功能包

https://blog.csdn.net/weixin_44858747/article/details/109479414

• [ 3] 安装kinectDK官方SDK ，和ros功能包

https://blog.csdn.net/qq_27399933/article/details/107356117

• [ 4] 需要在cmakelists文件和package.xml文件中添加opencv、pcl等库
  cmakelists.txt

find_package(catkin REQUIRED COMPONENTS
cv_bridge
image_transport
pcl_conversions
pcl_ros
roscpp
rospy
std_msgs
message_generation
)
find_package( PCL REQUIRED COMPONENTS common io visualization )

catkin_package(INCLUDE_DIRS include
LIBRARIES object_detection
CATKIN_DEPENDS
roscpp std_msgs message_generation message_runtime
)
include_directories(
$ {catkin_INCLUDE_DIRS} $ {PCL_INCLUDE_DIRS}
)
link_directories($ {PCL_LIBRARY_DIRS})
add_definitions($ {PCL_DEFINITIONS})
include_directories("./include/")
add_executable(main src/main.cpp include/pointcloud_seg/pointcloud_seg.h src/pointcloud_seg.cpp) 改变这部分
target_link_libraries(main ${catkin_LIBRARIES} ${PCL_LIBRARIES})

package.xml(有些可能不用，但可以跑)

catkin
cv_bridge
image_transport
pcl_conversions
pcl_ros
roscpp
sensor_msgs
std_msgs
libpcl-all-dev
message_generation
cv_bridge
image_transport
pcl_conversions
pcl_ros
roscpp
sensor_msgs
std_msgs
libpcl-all
message_generation
cv_bridge
image_transport
pcl_conversions
pcl_ros
roscpp
sensor_msgs
std_msgs
libpcl-all
message_runtime

$ roslaunch azure_kinect_ros_driver driver.launch 

1.创建类

#include  #include  #include  #include  #include  #include  #include  #include  #include  #include  using namespace std; using namespace ros; using namespace pcl; typedef pcl::PointXYZRGB PointType; class pointcloud_seg { 
           public: ros::Subscriber sub; public: pointcloud_seg(ros::NodeHandle &n); void get_pointcloud_cb(const sensor_msgs::PointCloud2& cloud_msg); ~pointcloud_seg(); }; pointcloud_seg::pointcloud_seg(ros::NodeHandle &n) { 
           //用于接受点云的节点和发布点云的节点 接受官方ros包发出的点云节点/points2 sub=n.subscribe("/points2",1,&pointcloud_seg::get_pointcloud_cb,this); //一会用于发布平面点，和平面之外的点，如果不发布进行平面提取等结果，不需要定义 pub_plant=n.advertise<sensor_msgs::PointCloud2>("Plant",1000); //发布平面点 pub_obj=n.advertise<sensor_msgs::PointCloud2>("Object",1000); //发布平面外的点 } //接受点云的回调函数 void pointcloud_seg::get_pointcloud_cb(const sensor_msgs::PointCloud2 &cloud_msg) { 
           pcl::PointCloud<PointType>::Ptr cloud(new pcl::PointCloud<PointType>); //ros获取的点云是pointcloud2类型的点云，需要转换为pcl类型的点云 pcl::fromROSMsg(cloud_msg,*cloud); //cloud就是获取到的转换为pcl::PointXYZRGB的点云 //可以在这部分对点云进行处理，包括滤波等操作 } pointcloud_seg::~pointcloud_seg() { 
           } int main(int argc, char **argv) { 
           // Initialize ROS ros::init(argc, argv, "pointcloud_seg"); ros::NodeHandle n; //定义类 pointcloud_seg seg(n); ros::spin(); return 0; } 

1.进行XYZ方向的直通滤波，可以保证点云的X,Y,Z范围在我们想要的范围内
需要头文件

#include

    ///进行X方向的范围限制，点云中的所有点的x都在-1m到+1米
    pcl::PointCloud<PointType>::Ptr cloudPtrx(new pcl::PointCloud<PointType>);
    pcl::PassThrough<PointType> px_filter;
    //input点云
    px_filter.setInputCloud(cloud_msg_xyz); // Pass filtered_cloud to the filter
    px_filter.setFilterFieldName("x"); // Set axis x
    px_filter.setFilterLimits(-1, 1); // Set limits min_value to max_value
    //output点云
    px_filter.filter(*cloudPtrx); // Restore output data in second_cloud
	
    // Perform the PassThrough Filter to the Y axis
    pcl::PointCloud<PointType>::Ptr cloudPtry(new pcl::PointCloud<PointType>);
    pcl::PassThrough<PointType> py_filter;
    py_filter.setInputCloud(cloudPtrx); // Pass filtered_cloud to the filter
    py_filter.setFilterFieldName("y"); // Set axis y
    py_filter.setFilterLimits(-1, 1); // Set limits min_value to max_value
    py_filter.filter(*cloudPtry); // Restore output data in second_cloud

    // Perform the PassThrough Filter to the Z axis
    pcl::PointCloud<PointType>::Ptr cloudPtrz(new pcl::PointCloud<PointType>);
    pcl::PassThrough<PointType> pz_filter;
    pz_filter.setInputCloud(cloudPtry); // Pass filtered_cloud to the filter
    pz_filter.setFilterFieldName("z"); // Set axis z
    pz_filter.setFilterLimits(0, 1.5); // Set limits min_value to max_value
    pz_filter.filter(*cloudPtrz); // Restore output data in second_cloud
}
//最后处理过的点云cloudPtrz x属于【-1，1】，y属于【-1，1】，z属于【0，1.5】 单位为m

2.进行体素滤波，目的是进行下采样，控制点云的密度
需要头文件

#include

 // Perform the VoxelGrid Filter
    pcl::PointCloud<PointType>::Ptr cloudPtrv(new pcl::PointCloud<PointType>);
    pcl::VoxelGrid<PointType> v_filter;
    v_filter.setInputCloud(cloudPtrz); // Pass raw_cloud to the filter
    //设置体素网格的大小，越大点越少
    v_filter.setLeafSize(0.01, 0.01,0.01); // Set leaf size
    v_filter.filter(*cloudPtrv); // Store output data in first_cloud

3.去除离群点
需要头文件

#include

需要转换为pointcloud2类型的数据

        sensor_msgs::PointCloud2 output;//声明的输出的点云的格式
        pcl::toROSMsg(*cloud_filtered, output);//第一个参数是输入，后面的是输出
        pub_obj.publish(output);

ROS中pcl的的转换代码

https://blog.csdn.net/u010284636/article/details/79214841

打开rviz查看 查看output

rviz

代码小白，欢迎大家批评指正。