Fast Point Feature Histograms (FPFH)
执行效率慢，占用大量CPU
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
// Create the FPFH estimation class, and pass the input dataset+normals to it
pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh;
//使用OMP多线程加速执行，待验证
//pcl::FPFHEstimationOMP<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh;
//fpfh.setNumberOfThreads(8);
fpfh.setInputCloud(cloud);
fpfh.setInputNormals(normals);
// alternatively, if cloud is of tpe PointNormal, do fpfh.setInputNormals (cloud);
// Create an empty kdtree representation, and pass it to the FPFH estimation object.
// Its content will be filled inside the object, based on the given input dataset (as no other search surface is given).
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
fpfh.setSearchMethod(tree);
// Output datasets
pcl::PointCloud<pcl::FPFHSignature33>::Ptr fpfhs(new pcl::PointCloud<pcl::FPFHSignature33>());
// Use all neighbors in a sphere of radius 5cm
// IMPORTANT: the radius used here has to be larger than the radius used to estimate the surface normals!!!
fpfh.setRadiusSearch(0.05);
// Compute the features
fpfh.compute(*fpfhs);