纯虚函数，具体实现在其子类KdTreeFLANN中，其用来进行K 领域搜索，k_sqr_distances 为搜索完成后每个邻域点与查询点的欧式距离，

#include <pcl/point_cloud.h>        //点类型定义头文件
#include <pcl/kdtree/kdtree_flann.h> //kdtree类定义头文件

#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//创建一个PointCloud<pcl::PointXYZ>pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);// 随机点云生成cloud->width = 1000;             //此处点云数量cloud->height = 1;                //表示点云为无序点云cloud->points.resize (cloud->width * cloud->height);for (size_t i = 0; i < cloud->points.size (); ++i)   //循环填充点云数据
  {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);}//创建KdTreeFLANN对象，并把创建的点云设置为输入,创建一个searchPoint变量作为查询点pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;//设置搜索空间
  kdtree.setInputCloud (cloud);//设置查询点并赋随机值
  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);// K 临近搜索//创建一个整数（设置为10）和两个向量来存储搜索到的K近邻，两个向量中，一个存储搜索到查询点近邻的索引，另一个存储对应近邻的距离平方int K = 10;std::vector<int> pointIdxNKNSearch(K);      //存储查询点近邻索引std::vector<float> pointNKNSquaredDistance(K); //存储近邻点对应距离平方//打印相关信息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 )  //执行K近邻搜索
  {//打印所有近邻坐标for (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;}/**********************************************************************************下面的代码展示查找到给定的searchPoint的某一半径（随机产生）内所有近邻，重新定义两个向量pointIdxRadiusSearch  pointRadiusSquaredDistance来存储关于近邻的信息********************************************************************************/// 半径 R内近邻搜索方法
std::vector<int> pointIdxRadiusSearch;           //存储近邻索引std::vector<float> pointRadiusSquaredDistance;   //存储近邻对应距离的平方float radius = 256.0f * rand () / (RAND_MAX + 1.0f);   //随机的生成某一半径//打印输出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 )  //执行半径R内近邻搜索方法
  {for (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;
}