《PCL》kdtree

本文主要是介绍《PCL》kdtree，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

若searchPoint为待搜索点云之外，则…
若searchPoint为待搜索点云之内，则第一个点为其本身，已经验证过。

1. K搜索

#include <pcl/point_cloud.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <vector>pcl::KdTreeFLANN<pcl::PointXYZ>kdtree;
kdtree.setInputCloud(cloud);
pcl::PointXYZ searchPoint;
int K = 10;
std::vector<int>pointIdxNKNSearch(K);
std::vector<float>Distance(K);
kdtree.nearestKSearch(searchPoint, K, pointIdxNKNSearch, Distance) 

2. 半径r搜索

#include <pcl/point_cloud.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <vector>pcl::KdTreeFLANN<pcl::PointXYZ>kdtree;
kdtree.setInputCloud(cloud);
pcl::PointXYZ searchPoint;
float radius;
std::vector<int> pointIdxRadiusSearch;
std::vector<float> Distance;
kdtree.radiusSearch(searchPoint, radius, pointIdxRadiusSearch, Distance)

3. 对点云进行索引计算
   此时，当keypoints为全部点云时，即计算索引。

#include <pcl/point_cloud.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <vector>void getIndices (pointcloud::Ptr cloudin, pointcloud keypoints, pcl::PointIndices::Ptr indices)
{pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;kdtree.setInputCloud(cloudin);std::vector<float>pointNKNSquareDistance; //近邻点集的距离std::vector<int> pointIdxNKNSearch;for (size_t i =0; i < keypoints.size();i++){kdtree.nearestKSearch(keypoints.points[i],1,pointIdxNKNSearch,pointNKNSquareDistance);// cout<<"the distance is:"<<pointNKNSquareDistance[0]<<endl;// cout<<"the indieces is:"<<pointIdxNKNSearch[0]<<endl;indices->indices.push_back(pointIdxNKNSearch[0]);     }
}

4.FLANN近邻查找 获取点云平均距离
只是 计算了 前一半索引号的点云距离平均值。

pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;
kdtree.setInputCloud(cloud);
int K=2;
float allDistance=0 ,average = 0;
for(size_t i=0; i < cloud.size()/2; i++)
{vector<int> indicexK;vector<float> distanceK;kdtree.nearestKSearch(cloud->point[i], K, indicesK, distanceK);allDistance +=sqrt(distanceK[1]);
}
average = allDistance / cloud.size()/2;

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