激光雷达点云数据处理

步骤：

1. 通过rosbag录制激光雷达数据

2. 将rosbag转为pcd格式，一帧一个文件

3. 解析pcd文件数据

 

详细步骤

1. 使用速腾16线激光雷达
   1. cd到激光雷达程序目录，运行launch文件
   2. rostopic list 查看topic信息，然后rosbag record /rslidar_points，会在对应目录下生成一个以时间命名的文件，里面记录 的激光雷达点云数据，链接：http://wiki.ros.org/ROS/Tutorials/Recording%20and%20playing%20back%20data
2. 通过以下命令来将rosbag转换为pcd

   rosrun pcl_ros bag_to_pcd Desktop/2018-10-10-14-19-26.bag /rslidar_points Desktop/pcd

                                                     rosbag路径                                      需要转换的topic  pcd文件存储路径

3. 通过以下代码将pcd文件解析出来，并且通过pcl viewer将那一帧的点云数据可视化

   #include  
   #include  
   #include  
   #include  
   #include  
   using namespace std; 
   
   int main (int argc, char** argv){ 
   typedef pcl::PointXYZRGBA PointT; 
   pcl::PointCloud::Ptr cloud (new pcl::PointCloud); 
   
   std::string dir = "/home/deanjin/Desktop/pcd/"; 
   std::string filename = "1539152366.934292000.pcd"; 
   
   if (pcl::io::loadPCDFile ((dir+filename), *cloud) == -1){ 
   //* load the file 
   PCL_ERROR ("Couldn't read PCD file \n"); 
   return (-1); 
   } 
   printf("Loaded %d data points from PCD\n", 
   cloud->width * cloud->height); 
   
   printf("points size is %ld\n", cloud->points.size());
   
   for (size_t i = 0; i < cloud->points.size (); i+=10000) 
   printf("%8.3f %8.3f %8.3f %5d %5d %5d %5d\n", 
   cloud->points[i].x, 
   cloud->points[i].y, 
   cloud->points[i].z, 
   cloud->points[i].r, 
   cloud->points[i].g, 
   cloud->points[i].b, 
   cloud->points[i].a 
   ); 
   
   pcl::visualization::PCLVisualizer viewer("Cloud viewer"); 
   viewer.setCameraPosition(0,0,-3.0,0,-1,0); 
   viewer.addCoordinateSystem(0.3); 
   
   viewer.addPointCloud(cloud); 
   while(!viewer.wasStopped()) 
   viewer.spinOnce(100); 
   return (0); 
   } 

   CmakeList.txt

   cmake_minimum_required(VERSION 2.8.3)
   project(test)
   find_package(PCL REQUIRED) 
   include_directories(${PCL_INCLUDE_DIRS}) 
   add_executable(test test.cpp)
   target_link_libraries(test ${PCL_LIBRARIES})