PCL——（5）kd-tree实现快速领域搜索

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#include <pcl/point_cloud.h>
#include <pcl/kdtree/kdtree_flann.h>

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

int main (int argc, char** argv)
{
  srand (time (NULL));//用系统时间初始化随机种子

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

  // Generate pointcloud data
 // 随机点云生成
  cloud->width=1000;                 //此处为点云数量
  cloud->height=1;                   //此处表示点云为无序点云
  cloud->points.resize (cloud->width * cloud->height);
  // //循环填充点云数据
  for (std::size_t i = 0; i < cloud->points.size (); ++i)
  {
    cloud->points[i].x = 1024.0f * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].y = 1024.0f * rand () / (RAND_MAX + 1.0f);
    cloud->points[i].z = 1024.0f * rand () / (RAND_MAX + 1.0f);
  }

  pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;//创建kdtree对象

  kdtree.setInputCloud(cloud); // 设置搜索点云(空间)

  pcl::PointXYZ searchPoint;//定义需要查询的点并赋随机值

  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);

  // K nearest neighbor search
  int K = 10;

  std::vector<int> pointIdxNKNSearch(K);//存储查询点近邻索引
  std::vector<float> pointNKNSquaredDistance(K);//存储近邻点对应平方距离

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

  if ( kdtree.nearestKSearch (searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0 )
  {
   //打印出所有近邻坐标
    for (std::size_t i = 0; i < pointIdxNKNSearch.size (); ++i)
      std::cout << "    "  <<   cloud->points[ pointIdxNKNSearch[i] ].x 
                << " " << cloud->points[ pointIdxNKNSearch[i] ].y 
                << " " << cloud->points[ pointIdxNKNSearch[i] ].z 
                << " (squared distance: " << pointNKNSquaredDistance[i] << ")" << std::endl;
  }

  // Neighbors within radius search
  std::vector<int> pointIdxRadiusSearch; //存储近邻索引
  std::vector<float> pointRadiusSquaredDistance;  //存储近邻对应的平方距离

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


  return 0;
}

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