【PCL库+ubuntu+C++】 2.使用PCL实现对点云的变换

• 简介
• • 思路
• 源码
• • CMakeLists.txt
  • matrix_transform.cpp

本篇用一个4X4的矩阵,对点云数据进行平移和旋转变换,看到自动驾驶中对点云进行变换的时候用到的比较多

1. 引入库函数

#include  

该头文件包含变换矩阵的API.
2. 程序的帮助文档,-h,–help都可以激活,写这个函数是一个优秀的习惯:

// This function displays the help
void showHelp(char * program_name)
{
  std::cout << std::endl;
  std::cout << "Usage: " << program_name << " cloud_filename.[pcd|ply]" << std::endl;
  std::cout << "-h:  Show this help." << std::endl;
}
...此处省略了很多代码...
// Show help
  if (pcl::console::find_switch (argc, argv, "-h") || pcl::console::find_switch (argc, argv, "--help")) {
    showHelp (argv[0]);
    return 0;
  }

3. 从命令行接收参数(点云文件格式的)

// Fetch point cloud filename in arguments | Works with PCD and PLY files
  std::vector filenames;
  bool file_is_pcd = false;

  filenames = pcl::console::parse_file_extension_argument (argc, argv, ".ply");

  if (filenames.size () != 1)  
  {
    filenames = pcl::console::parse_file_extension_argument (argc, argv, ".pcd");

    if (filenames.size () != 1) 
    {
      showHelp (argv[0]);
      return -1;
    } 
    else 
    {
      file_is_pcd = true;
    }
  }

4. 初始化一个4X4的矩阵

  Eigen::Matrix4f transform_1 = Eigen::Matrix4f::Identity();
  

5. 为旋转矩阵赋值,它的3阶是旋转矩阵需要的,值得一提的是,这里对旋转矩阵的构造不止一种,选取一种作为示例,其余请读者据官网自行探索.

// Define a rotation matrix (see https://en.wikipedia.org/wiki/Rotation_matrix)
  float theta = M_PI/4; // The angle of rotation in radians
  transform_1 (0,0) = std::cos (theta);
  transform_1 (0,1) = -sin(theta);
  transform_1 (1,0) = sin (theta);
  transform_1 (1,1) = std::cos (theta);
  //    (row, column)

  // Define a translation of 2.5 meters on the x axis.
  transform_1 (0,3) = 2.5;

  // Print the transformation
  printf ("Method #1: using a Matrix4fn");
  std::cout << transform_1 << std::endl;

6. 开始变换

// Executing the transformation
  pcl::PointCloud::Ptr transformed_cloud (new pcl::PointCloud ());
  // You can either apply transform_1 or transform_2; they are the same
  pcl::transformPointCloud (*source_cloud, *transformed_cloud, transform_1);

7. 然后就可以通过可视化模块PCLVisualizer,可视出来
8. 最后编译,查看,就可以了,下边是执行效果(记得要带参数即需要变换的点云格式图片执行哦),这里我的点云文件是cube.ply:

./matrix_transform cube.ply 

命令行输出

效果pcl输出

cmake_minimum_required(VERSION 3.5 FATAL_ERROR)
 
project(pcl-matrix_transform)

find_package(PCL 1.7 REQUIRED)

include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})

add_executable (matrix_transform matrix_transform.cpp)
target_link_libraries (matrix_transform ${PCL_LIBRARIES})

#include 
#include 
#include 
#include 
#include 
#include 
#include 

// This function displays the help
void showHelp(char * program_name)
{
  std::cout << std::endl;
  std::cout << "Usage: " << program_name << " cloud_filename.[pcd|ply]" << std::endl;
  std::cout << "-h:  Show this help." << std::endl;
}
// This is the main function
int main (int argc, char** argv)
{

  // Show help
  if (pcl::console::find_switch (argc, argv, "-h") || pcl::console::find_switch (argc, argv, "--help")) 
  {
   showHelp (argv[0]);
    return 0;
  }

  // Fetch point cloud filename in arguments | Works with PCD and PLY files
   std::vector filenames;
   bool file_is_pcd = false;
 
   filenames = pcl::console::parse_file_extension_argument (argc, argv, ".ply");
 
   if (filenames.size () != 1)  
   {
     filenames = pcl::console::parse_file_extension_argument (argc, argv, ".pcd");
 
     if (filenames.size () != 1) 
     {
       showHelp (argv[0]);
       return -1;
     } 
     else 
     {
       file_is_pcd = true;
     }
   }
 
   // Load file | Works with PCD and PLY files
   pcl::PointCloud::Ptr source_cloud (new pcl::PointCloud ());
 
   if (file_is_pcd) 
   {
     if (pcl::io::loadPCDFile (argv[filenames[0]], *source_cloud) < 0)  
     {
       std::cout << "Error loading point cloud " << argv[filenames[0]] << std::endl << std::endl;
       showHelp (argv[0]);
       return -1;
     }
   } 
   else 
   {
     if (pcl::io::loadPLYFile (argv[filenames[0]], *source_cloud) < 0) 
      {
       std::cout << "Error loading point cloud " << argv[filenames[0]] << std::endl << std::endl;
       showHelp (argv[0]);
       return -1;
     }
   }
 
   
   Eigen::Matrix4f transform_1 = Eigen::Matrix4f::Identity();
 
   // Define a rotation matrix (see https://en.wikipedia.org/wiki/Rotation_matrix)
   float theta = M_PI/4; // The angle of rotation in radians
   transform_1 (0,0) = std::cos (theta);
   transform_1 (0,1) = -sin(theta);
   transform_1 (1,0) = sin (theta);
   transform_1 (1,1) = std::cos (theta);
   //    (row, column)
 
   // Define a translation of 2.5 meters on the x axis.
   transform_1 (0,3) = 2.5;
 
   // Print the transformation
   printf ("Method #1: using a Matrix4fn");
   std::cout << transform_1 << std::endl;
 
   
   Eigen::Affine3f transform_2 = Eigen::Affine3f::Identity();
 
   // Define a translation of 2.5 meters on the x axis.
   transform_2.translation() << 2.5, 0.0, 0.0;
 
  // The same rotation matrix as before; theta radians around Z axis
  transform_2.rotate (Eigen::AngleAxisf (theta, Eigen::Vector3f::UnitZ()));

  // Print the transformation
  printf ("nMethod #2: using an Affine3fn");
  std::cout << transform_2.matrix() << std::endl;

 // Executing the transformation
 pcl::PointCloud::Ptr transformed_cloud (new pcl::PointCloud ());
  // You can either apply transform_1 or transform_2; they are the same
  pcl::transformPointCloud (*source_cloud, *transformed_cloud, transform_2);

  // Visualization
  printf(  "nPoint cloud colors :  white  = original point cloudn"
      "                        red  = transformed point cloudn");
  pcl::visualization::PCLVisualizer viewer ("Matrix transformation example");

   // Define R,G,B colors for the point cloud
  pcl::visualization::PointCloudColorHandlerCustom source_cloud_color_handler (source_cloud, 255, 255, 255);
  // We add the point cloud to the viewer and pass the color handler
  viewer.addPointCloud (source_cloud, source_cloud_color_handler, "original_cloud");

  pcl::visualization::PointCloudColorHandlerCustom transformed_cloud_color_handler (transformed_cloud, 230, 20, 20); // Red
  viewer.addPointCloud (transformed_cloud, transformed_cloud_color_handler, "transformed_cloud");

  viewer.addCoordinateSystem (1.0, "cloud", 0);
  viewer.setBackgroundColor(0.05, 0.05, 0.05, 0); // Setting background to a dark grey
  viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "original_cloud");
  viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "transformed_cloud");
  //viewer.setPosition(800, 400); // Setting visualiser window position

  while (!viewer.wasStopped ()) 
  { // Display the visualiser until 'q' key is pressed
    viewer.spinOnce ();
  }

  return 0;
}