feature_estimation.h
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00001 #ifndef FEATURE_ESTIMATION_H
00002 #define FEATURE_ESTIMATION_H
00003 
00004 #include "typedefs.h"
00005 
00006 #include <pcl/io/io.h>
00007 #include <pcl/features/normal_3d.h>
00008 #include <pcl/keypoints/sift_keypoint.h>
00009 #include <pcl/features/fpfh.h>
00010 #include <pcl/features/vfh.h>
00011 
00012 /* Use NormalEstimation to estimate a cloud's surface normals 
00013  * Inputs:
00014  *   input
00015  *     The input point cloud
00016  *   radius
00017  *     The size of the local neighborhood used to estimate the surface
00018  * Return: A pointer to a SurfaceNormals point cloud
00019  */
00020 SurfaceNormalsPtr
00021 estimateSurfaceNormals (const PointCloudPtr & input, float radius)
00022 {
00023   SurfaceNormalsPtr normals;
00024   return (normals);
00025 }
00026 
00027 /* Use SIFTKeypoint to detect a set of keypoints
00028  * Inputs:
00029  *   points
00030  *     The input point cloud
00031  *   normals
00032  *     The input surface normals
00033  *   min_scale
00034  *     The smallest scale in the difference-of-Gaussians (DoG) scale-space
00035  *   nr_octaves
00036  *     The number of times the scale doubles in the DoG scale-space
00037  *   nr_scales_per_octave
00038  *     The number of scales computed for each doubling
00039  *   min_contrast
00040  *     The minimum local contrast that must be present for a keypoint to be detected
00041  * Return: A pointer to a point cloud of keypoints
00042  */
00043 PointCloudPtr
00044 detectKeypoints (const PointCloudPtr & points, const SurfaceNormalsPtr & normals,
00045                  float min_scale, int nr_octaves, int nr_scales_per_octave, float min_contrast)  
00046 {
00047   PointCloudPtr keypoints;
00048   return (keypoints);
00049 }
00050 
00051 /* Use FPFHEstimation to compute local feature descriptors around each keypoint
00052  * Inputs:
00053  *   points
00054  *     The input point cloud
00055  *   normals
00056  *     The input surface normals
00057  *   keypoints
00058  *     A cloud of keypoints specifying the positions at which the descriptors should be computed
00059  *   feature_radius
00060  *     The size of the neighborhood from which the local descriptors will be computed 
00061  * Return: A pointer to a LocalDescriptors (a cloud of LocalDescriptorT points)
00062  */
00063 LocalDescriptorsPtr
00064 computeLocalDescriptors (const PointCloudPtr & points, const SurfaceNormalsPtr & normals, 
00065                          const PointCloudPtr & keypoints, float feature_radius)
00066 {
00067   LocalDescriptorsPtr local_descriptors;
00068   return (local_descriptors);
00069 }
00070 
00071 /* Use VFHEstimation to compute a single global descriptor for the entire input cloud
00072  * Inputs:
00073  *   points
00074  *     The input point cloud
00075  *   normals
00076  *     The input surface normals
00077  * Return: A pointer to a GlobalDescriptors point cloud (a cloud containing a single GlobalDescriptorT point)
00078  */
00079 GlobalDescriptorsPtr
00080 computeGlobalDescriptor (const PointCloudPtr & points, const SurfaceNormalsPtr & normals)
00081 {
00082   GlobalDescriptorsPtr global_descriptor;
00083   return (global_descriptor);
00084 }
00085 
00086 /* A simple structure for storing all of a cloud's features */
00087 struct ObjectFeatures
00088 {
00089   PointCloudPtr points;
00090   SurfaceNormalsPtr normals;
00091   PointCloudPtr keypoints;
00092   LocalDescriptorsPtr local_descriptors;
00093   GlobalDescriptorsPtr global_descriptor;
00094 };
00095 
00096 /* Estimate normals, detect keypoints, and compute local and global descriptors 
00097  * Return: An ObjectFeatures struct containing all the features
00098  */
00099 ObjectFeatures
00100 computeFeatures (const PointCloudPtr & input)
00101 {
00102   ObjectFeatures features;
00103   features.points = input;
00104   features.normals = estimateSurfaceNormals (input, 0.05);
00105   features.keypoints = detectKeypoints (input, features.normals, 0.005, 10, 8, 1.5);
00106   features.local_descriptors = computeLocalDescriptors (input, features.normals, features.keypoints, 0.1);
00107   features.global_descriptor = computeGlobalDescriptor (input, features.normals);
00108 
00109   return (features);
00110 }
00111 
00112 #endif


pcl
Author(s): Open Perception
autogenerated on Mon Oct 6 2014 03:15:05