io.hpp
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00039 
00040 #ifndef PCL_KDTREE_IO_IMPL_HPP_
00041 #define PCL_KDTREE_IO_IMPL_HPP_
00042 
00043 #include <pcl/kdtree/io.h>
00044 #include <pcl/kdtree/kdtree_flann.h>
00045 
00047 template <typename Point1T, typename Point2T> void
00048 pcl::getApproximateIndices (
00049     const typename pcl::PointCloud<Point1T>::Ptr &cloud_in, 
00050     const typename pcl::PointCloud<Point2T>::Ptr &cloud_ref, 
00051     std::vector<int> &indices)
00052 {
00053   pcl::KdTreeFLANN<Point2T> tree;
00054   tree.setInputCloud (cloud_ref);
00055 
00056   std::vector<int> nn_idx (1);
00057   std::vector<float> nn_dists (1);
00058   indices.resize (cloud_in->points.size ());
00059   for (size_t i = 0; i < cloud_in->points.size (); ++i)
00060   {
00061     tree.nearestKSearch (*cloud_in, i, 1, nn_idx, nn_dists);
00062     indices[i] = nn_idx[0];
00063   }
00064 }
00065 
00067 template <typename PointT> void
00068 pcl::getApproximateIndices (
00069     const typename pcl::PointCloud<PointT>::Ptr &cloud_in, 
00070     const typename pcl::PointCloud<PointT>::Ptr &cloud_ref, 
00071     std::vector<int> &indices)
00072 {
00073   pcl::KdTreeFLANN<PointT> tree;
00074   tree.setInputCloud (cloud_ref);
00075 
00076   std::vector<int> nn_idx (1);
00077   std::vector<float> nn_dists (1);
00078   indices.resize (cloud_in->points.size ());
00079   for (size_t i = 0; i < cloud_in->points.size (); ++i)
00080   {
00081     tree.nearestKSearch (*cloud_in, i, 1, nn_idx, nn_dists);
00082     indices[i] = nn_idx[0];
00083   }
00084 }
00085 
00086 #endif // PCL_KDTREE_IO_IMPL_H_
00087 


pcl
Author(s): Open Perception
autogenerated on Wed Aug 26 2015 15:25:02