Kd树按空间划分生成叶子节点，各个叶子节点里存放点数据，其可以按半径搜索或邻区搜索。PCL中的Kd tree的基础数据结构使用了FLANN以便可以快速的进行邻区搜索。FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces。下面是一个最基本的例子，只寻找一个最近点：
#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)); //seeds rand() with the system time time_t begin,end;
begin = clock(); //开始计时
//-------------------------------------------------------------------------------
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>); // Generate pointcloud data
cloud->width = ;
cloud->height = ;
cloud->points.resize (cloud->width * cloud->height); // fills a PointCloud with random data
for (size_t i = ; 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);
} // creates kdtree object
pcl::KdTreeFLANN<pcl::PointXYZ> kdtree; // sets our randomly created cloud as the input
kdtree.setInputCloud (cloud); //create a “searchPoint” which is assigned random coordinates
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 nearest neighbor search
int K = ;
std::vector<int> pointIdxNKNSearch(K);
std::vector<float> pointNKNSquaredDistance(K); std::cout << "K nearest neighbor search at (" << searchPoint.x
<< " " << searchPoint.y
<< " " << searchPoint.z
<< ") with K=" << K << std::endl; /***********************************************************************************************
template<typename PointT>
virtual int pcl::KdTree< PointT >::nearestKSearch ( const PointT & p_q,
int k,
std::vector< int > & k_indices,
std::vector< float > & k_sqr_distances
) const [pure virtual] Search for k-nearest neighbors for the given query point.
Parameters:
[in] the given query point
[in] k the number of neighbors to search for
[out] the resultant indices of the neighboring points
[out] the resultant squared distances to the neighboring points
Returns:
number of neighbors found
********************************************************************************************/
if ( kdtree.nearestKSearch (searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > )
{
for (size_t i = ; 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;
}
//--------------------------------------------------------------------------------------------
end = clock(); //结束计时
double Times = double(end - begin) / CLOCKS_PER_SEC; //将clock()函数的结果转化为以秒为单位的量 std::cout<<"time: "<<Times<<"s"<<std::endl; return ;
}
生成四十万个随机点，release版本下测试0.3s左右找到最近点，这比之前自己写的Kd树不知快到哪里去了。当然自己写只是为了更好的理解其中的原理，真要用的时候还得靠别人的*...
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