点云库PCL学习笔记 -- k-d tree 与八叉树 -- 3.基于 Octree 八叉树的空间划分及邻域搜索（体素近邻搜索、K近邻搜索、半径r内近邻搜索）

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使用 Octree 八叉树的进行空间划分及邻域搜索（体素近邻搜索、K近邻搜索、半径r内近邻搜索）代码octree_search.cpp

#include 
#include 

#include 
#include 
#include 

int
main (int argc, char** argv)
{
  //用系统时间初始化随机种子
  srand ((unsigned int) time (NULL));
  
  //定义并实例化一个PointCloud指针对象，并用随机点集赋值给它
  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);

  //使用随机数生成点云数据
  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;	//设置八叉树分辨率 即体素的大小

  pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree (resolution);	//初始化八叉树

  octree.setInputCloud (cloud);			//设置输入点云
  octree.addPointsFromInputCloud ();	//构建八叉树

  //定义查询点，并用随机数对其进行赋值
  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);

  //体素内近邻搜索

  std::vector<int> pointIdxVec;		//存储体素近邻搜索的结果向量

  //执行搜索，返回结果到 pointIdxVec ，并对搜索到的结果进行打印
  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 近邻搜索

  int K = 10;	//设置搜索的 K 近邻数量为10

  std::vector<int> pointIdxNKNSearch;			//存储查询目标点的近邻索引
  std::vector<float> pointNKNSquaredDistance;	//存储近邻点对应的平方距离

  //打印查询目标点的相关信息
  std::cout << "K nearest neighbor search at (" << searchPoint.x 
            << " " << searchPoint.y 
            << " " << searchPoint.z
            << ") with K=" << K << std::endl;

  //如果 k-d tree 存在近邻点，则输出近邻点的相关位置信息和平方距离信息
  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;
  }

  //半径 r 内近邻搜索方式

  std::vector<int> pointIdxRadiusSearch;			//存储近邻索引（设置在半径 r 内的搜索近邻）
  std::vector<float> pointRadiusSquaredDistance;	//存储近邻点对应的平方距离

  float radius = 256.0f * rand () / (RAND_MAX + 1.0f);		//利用随机数产生近邻搜索半径r

  //打印随机生成的近邻搜索半径 r
  std::cout << "Neighbors within radius search at (" << searchPoint.x 
      << " " << searchPoint.y 
      << " " << searchPoint.z
      << ") with radius=" << radius << std::endl;

  //如果在半径 r 内存在近邻点，则输出近邻点的相关位置信息和平方距离信息
  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;
  }

}

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设置编译文件CMakeLists.txt

cmake_minimum_required(VERSION 2.8 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})

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编译

mkdir build
cd build/
cmake ..
make

执行程序

cd ..
./build/octree_search

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结果如下：

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