实现点云配准ICP算法以及个人代码理解

本文链接：https://blog.youkuaiyun.com/vhzfhf/article/details/129883310

前两篇 1.配置好了PCL库；2.会使用cmake编译源文件并查看结果

现在简单实现一下ICP算法，并理解代码

源代码在 PCL中的点云ICP配准（附源代码和数据）_pcl点云配准代码fish_Tom Hardy的博客-优快云博客将源码复制到新建的main.cpp文件中

新建CMakeLists.txt文件，并复制以下代码

project(pcl_icp)

add_executable(${PROJECT_NAME})
target_sources(${PROJECT_NAME}
    PRIVATE
        main.cpp
        )
find_package(PCL REQUIRED)
#include
target_include_directories(${PROJECT_NAME}
        PUBLIC
        ${PCL_INCLUDE_DIRS} )
#link
target_link_directories(${PROJECT_NAME}
    PUBLIC
        ${PCL_LIBRARY_DIRS}
        )
target_link_libraries(${PROJECT_NAME}
        ${PCL_LIBRARIES}
        )

 cmake_minimum_required(VERSION 3.26)

写好两个文件后用之前说过的方法cmake编译，一共得到如下

再下载实验用的点云模型，地址pcl点云模型_点云icp配准代码-VR其他资源-优快云文库

打开build中的sln文件，并打开bluid中的源码，修改第48行实验用的点云模型的绝对地址，运行

得到如下结果，按下空格迭代一次，迭代25次后，基本配准

源代码

#include <iostream>
#include <string>

#include <pcl/io/ply_io.h>
#include <pcl/point_types.h>
#include <pcl/registration/icp.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/console/time.h>   // TicToc

typedef pcl::PointXYZ PointT;
typedef pcl::PointCloud<PointT> PointCloudT;

bool next_iteration = false;

void
print4x4Matrix (const Eigen::Matrix4d & matrix)
{
  printf ("Rotation matrix :\n");
  printf ("    | %6.3f %6.3f %6.3f | \n", matrix (0, 0), matrix (0, 1), matrix (0, 2));
  printf ("R = | %6.3f %6.3f %6.3f | \n", matrix (1, 0), matrix (1, 1), matrix (1, 2));
  printf ("    | %6.3f %6.3f %6.3f | \n", matrix (2, 0), matrix (2, 1), matrix (2, 2));
  printf ("Translation vector :\n");
  printf ("t = < %6.3f, %6.3f, %6.3f >\n\n", matrix (0, 3), matrix (1, 3), matrix (2, 3));
}

void
keyboardEventOccurred (const pcl::visualization::KeyboardEvent& event,
                       void* nothing)
{
  if (event.getKeySym () == "space" && event.keyDown ())
    next_iteration = true;
}

int
main ()
{
  // The point clouds we will be using
  PointCloudT::Ptr cloud_in (new PointCloudT);  // Original point cloud
  PointCloudT::Ptr cloud_tr (new PointCloudT);  // Transformed point cloud
  PointCloudT::Ptr cloud_icp (new PointCloudT);  // ICP output point cloud


  int iterations = 1;  // Default number of ICP iterations


  pcl::console::TicToc time;
  time.tic ();
  if (pcl::io::loadPLYFile ("fish-2.ply", *cloud_in) < 0)
  {
    PCL_ERROR ("Error loading cloud %s.\n");
    return (-1);
  }
  std::cout << "\nLoaded file " << "fish-2.ply" << " (" << cloud_in->size () << " points) in " << time.toc () << " ms\n" << std::endl;

  // Defining a rotation matrix and translation vector
  Eigen::Matrix4d transformation_matrix = Eigen::Matrix4d::Identity ();

  // A rotation matrix (see https://en.wikipedia.org/wiki/Rotation_matrix)
  double theta = M_PI / 8;  // The angle of rotation in radians
  transformation_matrix (0, 0) = cos (theta);
  transformation_matrix (0, 1) = -sin (theta);
  transformation_matrix (1, 0) = sin (theta);
  transformation_matrix (1, 1) = cos (theta);

  // A translation on Z axis (0.4 meters)
  transformation_matrix (2, 3) = 0.4;

  // Display in terminal the transformation matrix
  std::cout << "Applying this rigid transformation to: cloud_in -> cloud_icp" << std::endl;
  print4x4Matrix (transformation_matrix);

  // Executing the transformation
  pcl::transformPointCloud (*cloud_in, *cloud_icp, transformation_matrix);
  *cloud_tr = *cloud_icp;  // We backup cloud_icp into cloud_tr for later use

  // The Iterative Closest Point algorithm
  time.tic ();
  pcl::IterativeClosestPoint<PointT, PointT> icp;
  icp.setMaximumIterations (iterations);
  icp.setInputSource (cloud_icp);
  icp.setInputTarget (cloud_in);
  icp.align (*cloud_icp);
  icp.setMaximumIterations (1);  // We set this variable to 1 for the next time we will call .align () function
  std::cout << "Applied " << iterations << " ICP iteration(s) in " << time.toc () << " ms" << std::endl;

  if (icp.hasConverged ())
  {
    std::cout << "\nICP has converged, score is " << icp.getFitnessScore () << std::endl;
    std::cout << "\nICP transformation " << iterations << " : cloud_icp -> cloud_in" << std::endl;
    transformation_matrix = icp.getFinalTransformation ().cast<double>();
    print4x4Matrix (transformation_matrix);
  }
  else
  {
    PCL_ERROR ("\nICP has not converged.\n");
    return (-1);
  }

  // Visualization
  pcl::visualization::PCLVisualizer viewer ("ICP demo");
  // Create two vertically separated viewports
  int v1 (0);
  int v2 (1);
  viewer.createViewPort (0.0, 0.0, 0.5, 1.0, v1);
  viewer.createViewPort (0.5, 0.0, 1.0, 1.0, v2);

  // The color we will be using
  float bckgr_gray_level = 0.0;  // Black
  float txt_gray_lvl = 1.0 - bckgr_gray_level;

  // Original point cloud is white
  pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_in_color_h (cloud_in, (int) 255 * txt_gray_lvl, (int) 255 * txt_gray_lvl,
                                                                             (int) 255 * txt_gray_lvl);
  viewer.addPointCloud (cloud_in, cloud_in_color_h, "cloud_in_v1", v1);
  viewer.addPointCloud (cloud_in, cloud_in_color_h, "cloud_in_v2", v2);

  // Transformed point cloud is green
  pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_tr_color_h (cloud_tr, 20, 180, 20);
  viewer.addPointCloud (cloud_tr, cloud_tr_color_h, "cloud_tr_v1", v1);

  // ICP aligned point cloud is red
  pcl::visualization::PointCloudColorHandlerCustom<PointT> cloud_icp_color_h (cloud_icp, 180, 20, 20);
  viewer.addPointCloud (cloud_icp, cloud_icp_color_h, "cloud_icp_v2", v2);

  // Adding text descriptions in each viewport
  viewer.addText ("White: Original point cloud\nGreen: Matrix transformed point cloud", 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_1", v1);
  viewer.addText ("White: Original point cloud\nRed: ICP aligned point cloud", 10, 15, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "icp_info_2", v2);

  std::stringstream ss;
  ss << iterations;
  std::string iterations_cnt = "ICP iterations = " + ss.str ();
  viewer.addText (iterations_cnt, 10, 60, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "iterations_cnt", v2);

  // Set background color
  viewer.setBackgroundColor (bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v1);
  viewer.setBackgroundColor (bckgr_gray_level, bckgr_gray_level, bckgr_gray_level, v2);

  // Set camera position and orientation
  viewer.setCameraPosition (-3.68332, 2.94092, 5.71266, 0.289847, 0.921947, -0.256907, 0);
  viewer.setSize (1280, 1024);  // Visualiser window size

  // Register keyboard callback :
  viewer.registerKeyboardCallback (&keyboardEventOccurred, (void*) NULL);

  // Display the visualiser
  while (!viewer.wasStopped ())
  {
    viewer.spinOnce ();

    // The user pressed "space" :
    if (next_iteration)
    {
      // The Iterative Closest Point algorithm
      time.tic ();
      icp.align (*cloud_icp);
      std::cout << "Applied 1 ICP iteration in " << time.toc () << " ms" << std::endl;

      if (icp.hasConverged ())
      {
        printf ("\033[11A");  // Go up 11 lines in terminal output.
        printf ("\nICP has converged, score is %+.0e\n", icp.getFitnessScore ());
        std::cout << "\nICP transformation " << ++iterations << " : cloud_icp -> cloud_in" << std::endl;
        transformation_matrix *= icp.getFinalTransformation ().cast<double>();  // WARNING /!\ This is not accurate! For "educational" purpose only!
        print4x4Matrix (transformation_matrix);  // Print the transformation between original pose and current pose

        ss.str ("");
        ss << iterations;
        std::string iterations_cnt = "ICP iterations = " + ss.str ();
        viewer.updateText (iterations_cnt, 10, 60, 16, txt_gray_lvl, txt_gray_lvl, txt_gray_lvl, "iterations_cnt");
        viewer.updatePointCloud (cloud_icp, cloud_icp_color_h, "cloud_icp_v2");
      }
      else
      {
        PCL_ERROR ("\nICP has not converged.\n");
        return (-1);
      }
    }
    next_iteration = false;
  }
  return (0);
}