example_normal_estimation.cpp
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00039 
00040 #include <iostream>
00041 
00042 #include <pcl/io/pcd_io.h>
00043 #include <pcl/point_types.h>
00044 #include <pcl/features/normal_3d.h>
00045 #include <pcl/kdtree/kdtree_flann.h>
00046 
00047 int
00048 main (int, char** argv)
00049 {
00050   std::string filename = argv[1];
00051   std::cout << "Reading " << filename << std::endl;
00052 
00053   pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
00054 
00055   if (pcl::io::loadPCDFile<pcl::PointXYZ> (filename, *cloud) == -1) // load the file
00056   {
00057     PCL_ERROR ("Couldn't read file");
00058     return -1;
00059   }
00060 
00061   std::cout << "points: " << cloud->points.size () << std::endl;
00062 
00063   // Create the normal estimation class, and pass the input dataset to it
00064   pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimation;
00065   normal_estimation.setInputCloud (cloud);
00066 
00067   // Create an empty kdtree representation, and pass it to the normal estimation object.
00068   // Its content will be filled inside the object, based on the given input dataset (as no other search surface is given).
00069   pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
00070   normal_estimation.setSearchMethod (tree);
00071 
00072   // Output datasets
00073   pcl::PointCloud<pcl::Normal>::Ptr cloud_normals (new pcl::PointCloud<pcl::Normal>);
00074 
00075   // Use all neighbors in a sphere of radius 3cm
00076   normal_estimation.setRadiusSearch (0.03);
00077 
00078   // Compute the features
00079   normal_estimation.compute (*cloud_normals);
00080 
00081   // cloud_normals->points.size () should have the same size as the input cloud->points.size ()
00082   std::cout << "cloud_normals->points.size (): " << cloud_normals->points.size () << std::endl;
00083   return 0;
00084 }


pcl
Author(s): Open Perception
autogenerated on Wed Aug 26 2015 15:23:36