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本文学习了一种快速寻找无序点云的邻近点方法，就是构造KD树。用树这种数据结构，能够忽略掉许多点的比较，提高搜索速度。原理部分这篇博客讲的非常详细： 以下是在阅读完博主博文之后，自己仿照写的一个寻找最近点算法。代码还是要自己多写，粘贴复制一时爽，用的时候就会痛苦万分，不断去找文件和文件夹。
两点说明： 1、如果设置的目标点为待搜索点云中的点，则搜索到的邻近点包含其本身； 2、以下为寻找K个最近点，还有一种是寻找一定范围类的邻近点，大家对比分析；
#include #include #include #include #include int main(){ srand(time(NULL)); time_t begin, end; begin = clock(); pcl::PointCloud ::Ptr cloud(new pcl::PointCloud ); cloud->height = 1; cloud->width = 100000; cloud->is_dense = true; cloud->resize(cloud->width*cloud->height); for (int i = 0; i < cloud->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); } // creats kdtree object pcl::KdTreeFLANN kdtree; //set input point cloud kdtree.setInputCloud(cloud); // set target point pcl::PointXYZ target; target.x = 1024.0f*rand() / (RAND_MAX + 1.0f); target.y = 1024.0f*rand() / (RAND_MAX + 1.0f); target.z = 1024.0f*rand() / (RAND_MAX + 1.0f); // set kdtree search int K = 10; std::vector pointIdxNKNSearch(K); std::vector pointNKNSquaredDistance(K); std::cout << "K nearset neighbor search at (" << target.x << " " << target.y << " " << target.z << "), with K = " << K << std::endl; if (kdtree.nearestKSearch(target, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0) { 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; } end = clock(); double time = (end - begin) * 1000 / CLOCKS_PER_SEC; std::cout << "time: " << time << "ms" << std::endl; return 0;}
在半径范围内的搜索方式：
double radius; if (kdtree.radiusSearch(searchpoint, radius, Idx, Distance) > 0) { for (size_t i = 0; i < Idx.size(); i++) { cout << cloud->points[Idx[i]].x << " " << cloud->points[Idx[i]].y << " " << cloud->points[Idx[i]].z << " " << Distance[i] << endl; } } 转载地址：http://ivyci.baihongyu.com/