PCL学习记录-12-Registration点云配准-1 (ICP,NDT方法)

最新推荐文章于 2025-06-21 08:42:15 发布

HarwardWu

最新推荐文章于 2025-06-21 08:42:15 发布

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分类专栏： PCL ROS学习历程

本文链接：https://blog.youkuaiyun.com/weixin_42503785/article/details/115259715

本博文主要介绍关于点云配准的ICP和NDT两种方法的实现代码,并贴出具有参考意义的博文地址.

关于点云配准的较好的参考博客 :

http://robot.czxy.com/docs/pcl/chapter03/registration/#ndt

https://blog.youkuaiyun.com/sru_alo/article/details/88285135

https://blog.youkuaiyun.com/yuxuan20062007/article/details/80914102

一. ICP 点云配准方法-迭代最近点算法(ICP)

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/registration/icp.h>  //!!Mar26: ICP method head file

/*Demo for Point cloud registration-ICP method
*       ICP: Iterative Closest Point
*       Step By Step: 1. Establish two saperate point cloud data with off-set
*                     2. Using ICP method to find the transformatio matrix between the two Point cloud data
*/
using namespace std;



int main(int argc, char **argv){
  
  // Step1: Establish two point cloud data with off-set
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_in(new pcl::PointCloud<pcl::PointXYZ>);
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_out(new pcl::PointCloud<pcl::PointXYZ>);
  


  // Step2: Assign random points into the point cloud data and show coordinate
  cloud_in ->width =5;
  cloud_in ->height =1;
  cloud_in ->is_dense =false;
  cloud_in ->points.resize(cloud_in ->width * cloud_in ->height);

  for(size_t i=0; i< cloud_in ->size(); ++i){
      cloud_in ->points[i].x = 1024 * rand() /(RAND_MAX +1.0f);
      cloud_in ->points[i].y = 1024 * rand() /(RAND_MAX +1.0f);
      cloud_in ->points[i].z = 1024 * rand() /(RAND_MAX +1.0f);
  }
  
  cout << "Saved " << cloud_in ->points.size() << "data points to input: " <<endl;
  for(size_t i=0; i< cloud_in ->size(); ++i){
      std::cout << "    " <<
                  cloud