# if ( kdtree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0 ) # { # for (size_t i = 0; i < pointIdxRadiusSearch.size (); ++i) # std::cout << " " << cloud->points[ pointIdxRadiusSearch[i] ].x # << " " << cloud->points[ point...
同样,和以前一样,如果我们的 KdTree 在指定半径内返回 0 个以上的邻居,它会打印出这些点的坐标,这些点已经存储在我们的向量中。 if kdtree.radiusSearch(searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0: for i in range(len(pointIdxRadiusSearch)): print(" ", cloud.x[pointI...
<<") with radius="<< radius <<std::endl;/* 调用 radiusSearch 函数 查找 半径内的 近邻 点 */if(kdtree.radiusSearch(searchPoint,radius,pointIdxRadiusSearch,pointRadiusSquaredDistance)>0) {//遍历 找 到的 点 将其坐标 与 距离 终端 打印for(size_ti=0; i<pointIdxRadiusSearch.size(); ++i)...
if (kdtree.radiusSearch(searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0) { for (std::size_t i = 0; i < pointIdxRadiusSearch.size(); ++i) std::cout << " 点" << (*cloud)[pointIdxRadiusSearch[i]].x << " " << (*cloud)[pointIdxRadiusSearch[i]].y ...
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....
是KdTree的派生类。 略过构造函数。 nearestKSearch() 实际上是层层包装,调用了flann_index_内部的knnSearch函数。flann_index_是一个FLANNIndex,是::flann::Index<Dist>的别名。这么看来,pcl的KdTree搜索只是对FLANN的一层包装。 radiusSearch()同理,是对flann::Index<Dist>->radiusSearch()的封装。
kdtree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) 显然,我们还需要一个算法把Idx里的点云数据提取出来进行重新着色之类的工作,代码可以写作: pcl::PointCloud<pcl::PointXYZ>::Ptr Npoints(new pcl::PointCloud<pcl::PointXYZ>); ...
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[ pointIdx...
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....
( kdtree.radiusSearch (searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0 ) { for (std::size_t i = 0; i < pointIdxRadiusSearch.size (); ++i) std::cout << " " << cloud->points[ pointIdxRadiusSearch[i] ].x << "...