3d激光雷达开发（八叉树）

Posted 2022-03-08 费晓行

【 声明：版权所有，欢迎转载，请勿用于商业用途。 联系信箱：feixiaoxing @163.com】

        和kd树一样，八叉树也是一种数据管理方式。如果二叉树分别由左子树和右子树两个分支，那么八叉树就有八个分支。选择这一种数据结构主要也是为了查找方便。参考代码可以查找这个链接，https://pcl.readthedocs.io/projects/tutorials/en/master/octree.html#octree-search

1、准备octree_search.cpp文件

#include <pcl/point_cloud.h>
#include <pcl/octree/octree_search.h>

#include <iostream>
#include <vector>
#include <ctime>

int
main ()

  srand ((unsigned int) time (NULL));

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);

  // Generate pointcloud data
  cloud->width = 1000;
  cloud->height = 1;
  cloud->points.resize (cloud->width * cloud->height);

  for (std::size_t i = 0; i < cloud->size (); ++i)
  
    (*cloud)[i].x = 1024.0f * rand () / (RAND_MAX + 1.0f);
    (*cloud)[i].y = 1024.0f * rand () / (RAND_MAX + 1.0f);
    (*cloud)[i].z = 1024.0f * rand () / (RAND_MAX + 1.0f);
  

  float resolution = 128.0f;

  pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree (resolution);

  octree.setInputCloud (cloud);
  octree.addPointsFromInputCloud ();

  pcl::PointXYZ searchPoint;

  searchPoint.x = 1024.0f * rand () / (RAND_MAX + 1.0f);
  searchPoint.y = 1024.0f * rand () / (RAND_MAX + 1.0f);
  searchPoint.z = 1024.0f * rand () / (RAND_MAX + 1.0f);

  // Neighbors within voxel search

  std::vector<int> pointIdxVec;

  if (octree.voxelSearch (searchPoint, pointIdxVec))
  
    std::cout << "Neighbors within voxel search at (" << searchPoint.x 
     << " " << searchPoint.y 
     << " " << searchPoint.z << ")" 
     << std::endl;
              
    for (std::size_t i = 0; i < pointIdxVec.size (); ++i)
   std::cout << "    " << (*cloud)[pointIdxVec[i]].x 
       << " " << (*cloud)[pointIdxVec[i]].y 
       << " " << (*cloud)[pointIdxVec[i]].z << std::endl;
  

  // K nearest neighbor search

  int K = 10;

  std::vector<int> pointIdxNKNSearch;
  std::vector<float> pointNKNSquaredDistance;

  std::cout << "K nearest neighbor search at (" << searchPoint.x 
            << " " << searchPoint.y 
            << " " << searchPoint.z
            << ") with K=" << K << std::endl;

  if (octree.nearestKSearch (searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0)
  
    for (std::size_t i = 0; i < pointIdxNKNSearch.size (); ++i)
      std::cout << "    "  <<   (*cloud)[ pointIdxNKNSearch[i] ].x 
                << " " << (*cloud)[ pointIdxNKNSearch[i] ].y 
                << " " << (*cloud)[ pointIdxNKNSearch[i] ].z 
                << " (squared distance: " << pointNKNSquaredDistance[i] << ")" << std::endl;
  

  // Neighbors within radius search

  std::vector<int> pointIdxRadiusSearch;
  std::vector<float> pointRadiusSquaredDistance;

  float radius = 256.0f * rand () / (RAND_MAX + 1.0f);

  std::cout << "Neighbors within radius search at (" << searchPoint.x 
      << " " << searchPoint.y 
      << " " << searchPoint.z
      << ") with radius=" << radius << std::endl;


  if (octree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0)
  
    for (std::size_t i = 0; i < pointIdxRadiusSearch.size (); ++i)
      std::cout << "    "  <<   (*cloud)[ pointIdxRadiusSearch[i] ].x 
                << " " << (*cloud)[ pointIdxRadiusSearch[i] ].y 
                << " " << (*cloud)[ pointIdxRadiusSearch[i] ].z 
                << " (squared distance: " << pointRadiusSquaredDistance[i] << ")" << std::endl;
  

2、代码分析

        和kd树一样，八叉树也支持最近点查找和半径查找。除此之外，八叉树还支持体素查找，这是额外的一种查找方法。

3、准备CMakeLists.txt文件

cmake_minimum_required(VERSION 3.5 FATAL_ERROR)

project(octree_search)

find_package(PCL 1.2 REQUIRED)

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

add_executable (octree_search octree_search.cpp)
target_link_libraries (octree_search $PCL_LIBRARIES)

4、生成sln文件，准备编译

        用cmake生成文件之后就可以开始编译了。

5、准备执行octree_search.exe，

        执行过程中，可能会出现缺少dll的情况，像这样，

         补全dll后，就可以正常执行exe文件了，