Point Cloud Library (PCL)  1.14.0-dev
object_recognition.h
1 #pragma once
2 
3 #include "typedefs.h"
4 
5 #include "solution/filters.h"
6 #include "solution/segmentation.h"
7 #include "solution/feature_estimation.h"
8 #include "solution/registration.h"
9 
10 #include <pcl/io/pcd_io.h>
11 #include <pcl/kdtree/kdtree_flann.h>
12 
14 {
15  // Filter parameters
16  float min_depth;
17  float max_depth;
21 
22  // Segmentation parameters
25  float cluster_tolerance;
26  int min_cluster_size;
27  int max_cluster_size;
28 
29  // Feature estimation parameters
31  float keypoints_min_scale;
36 
37  // Registration parameters
45 };
46 
47 struct ObjectModel
48 {
49  PointCloudPtr points;
50  PointCloudPtr keypoints;
51  LocalDescriptorsPtr local_descriptors;
52  GlobalDescriptorsPtr global_descriptor;
53 };
54 
56 {
57 public:
59  {}
60 
61  void
62  populateDatabase (const std::vector<std::string> & /*filenames*/)
63  {
64  }
65 
66  const ObjectModel &
67  recognizeObject (const PointCloudPtr & /*query_cloud*/)
68  {
69  int best_match = 0;
70  return (models_[best_match]);
71  }
72 
73  PointCloudPtr
74  recognizeAndAlignPoints (const PointCloudPtr & /*query_cloud*/)
75  {
76  PointCloudPtr output;
77  return (output);
78  }
79 
80  /* Construct an object model by filtering, segmenting, and estimating feature descriptors */
81  void
82  constructObjectModel (const PointCloudPtr & points, ObjectModel & output) const
83  {
84  output.points = applyFiltersAndSegment (points, params_);
85 
86  SurfaceNormalsPtr normals;
87  estimateFeatures (output.points, params_, normals, output.keypoints,
88  output.local_descriptors, output.global_descriptor);
89  }
90 
91 protected:
92  /* Apply a series of filters (threshold depth, downsample, and remove outliers) */
93  PointCloudPtr
94  applyFiltersAndSegment (const PointCloudPtr & input, const ObjectRecognitionParameters & params) const
95  {
96  PointCloudPtr cloud;
97  cloud = thresholdDepth (input, params.min_depth, params.max_depth);
98  cloud = downsample (cloud, params.downsample_leaf_size);
99  cloud = removeOutliers (cloud, params.outlier_rejection_radius, params.outlier_rejection_min_neighbors);
100 
101  cloud = findAndSubtractPlane (cloud, params.plane_inlier_distance_threshold, params.max_ransac_iterations);
102  std::vector<pcl::PointIndices> cluster_indices;
103  clusterObjects (cloud, params.cluster_tolerance, params.min_cluster_size,
104  params.max_cluster_size, cluster_indices);
105 
106  PointCloudPtr largest_cluster (new PointCloud);
107  pcl::copyPointCloud (*cloud, cluster_indices[0], *largest_cluster);
108 
109  return (largest_cluster);
110  }
111 
112  /* Estimate surface normals, keypoints, and local/global feature descriptors */
113  void
114  estimateFeatures (const PointCloudPtr & points, const ObjectRecognitionParameters & params,
115  SurfaceNormalsPtr & normals_out, PointCloudPtr & keypoints_out,
116  LocalDescriptorsPtr & local_descriptors_out, GlobalDescriptorsPtr & global_descriptor_out) const
117  {
118  normals_out = estimateSurfaceNormals (points, params.surface_normal_radius);
119 
120  keypoints_out = detectKeypoints (points, normals_out, params.keypoints_min_scale, params.keypoints_nr_octaves,
122 
123  local_descriptors_out = computeLocalDescriptors (points, normals_out, keypoints_out,
124  params.local_descriptor_radius);
125 
126  global_descriptor_out = computeGlobalDescriptor (points, normals_out);
127  }
128 
129  /* Align the points in the source model to the points in the target model */
130  PointCloudPtr
131  alignModelPoints (const ObjectModel & source, const ObjectModel & target,
132  const ObjectRecognitionParameters & params) const
133  {
134  Eigen::Matrix4f tform;
135  tform = computeInitialAlignment (source.keypoints, source.local_descriptors,
136  target.keypoints, target.local_descriptors,
140 
141  tform = refineAlignment (source.points, target.points, tform,
144 
145  PointCloudPtr output (new PointCloud);
146  pcl::transformPointCloud (*(source.points), *output, tform);
147 
148  return (output);
149  }
150 
152  std::vector<ObjectModel> models_;
153  GlobalDescriptorsPtr descriptors_;
155 };
pcl::KdTreeFLANN< GlobalDescriptorT >::Ptr kdtree_
PointCloudPtr applyFiltersAndSegment(const PointCloudPtr &input, const ObjectRecognitionParameters &params) const
std::vector< ObjectModel > models_
const ObjectModel & recognizeObject(const PointCloudPtr &)
GlobalDescriptorsPtr descriptors_
ObjectRecognition(const ObjectRecognitionParameters &params)
PointCloudPtr recognizeAndAlignPoints(const PointCloudPtr &)
ObjectRecognitionParameters params_
PointCloudPtr alignModelPoints(const ObjectModel &source, const ObjectModel &target, const ObjectRecognitionParameters &params) const
void populateDatabase(const std::vector< std::string > &)
void constructObjectModel(const PointCloudPtr &points, ObjectModel &output) const
void estimateFeatures(const PointCloudPtr &points, const ObjectRecognitionParameters &params, SurfaceNormalsPtr &normals_out, PointCloudPtr &keypoints_out, LocalDescriptorsPtr &local_descriptors_out, GlobalDescriptorsPtr &global_descriptor_out) const
shared_ptr< KdTreeFLANN< PointT, Dist > > Ptr
Definition: kdtree_flann.h:151
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
void transformPointCloud(const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform, bool copy_all_fields)
Apply a rigid transform defined by a 4x4 matrix.
Definition: transforms.hpp:221
void copyPointCloud(const pcl::PointCloud< PointInT > &cloud_in, pcl::PointCloud< PointOutT > &cloud_out)
Copy all the fields from a given point cloud into a new point cloud.
Definition: io.hpp:142
GlobalDescriptorsPtr global_descriptor
LocalDescriptorsPtr local_descriptors
PointCloudPtr keypoints
PointCloudPtr points