Point Cloud Library (PCL)  1.12.1-dev
ppf_registration.h
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40 
41 #pragma once
42 
43 #include <pcl/features/ppf.h>
44 #include <pcl/registration/registration.h>
45 
46 #include <unordered_map>
47 
48 namespace pcl {
50 public:
51  /** \brief Data structure to hold the information for the key in the feature hash map
52  * of the PPFHashMapSearch class \note It uses multiple pair levels in order to enable
53  * the usage of the boost::hash function which has the std::pair implementation (i.e.,
54  * does not require a custom hash function)
55  */
56  struct HashKeyStruct : public std::pair<int, std::pair<int, std::pair<int, int>>> {
57  HashKeyStruct() = default;
58 
59  HashKeyStruct(int a, int b, int c, int d)
60  {
61  this->first = a;
62  this->second.first = b;
63  this->second.second.first = c;
64  this->second.second.second = d;
65  }
66 
67  std::size_t
68  operator()(const HashKeyStruct& s) const noexcept
69  {
70  const std::size_t h1 = std::hash<int>{}(s.first);
71  const std::size_t h2 = std::hash<int>{}(s.second.first);
72  const std::size_t h3 = std::hash<int>{}(s.second.second.first);
73  const std::size_t h4 = std::hash<int>{}(s.second.second.second);
74  return h1 ^ (h2 << 1) ^ (h3 << 2) ^ (h4 << 3);
75  }
76  };
78  std::unordered_multimap<HashKeyStruct,
79  std::pair<std::size_t, std::size_t>,
81  using FeatureHashMapTypePtr = shared_ptr<FeatureHashMapType>;
82  using Ptr = shared_ptr<PPFHashMapSearch>;
83  using ConstPtr = shared_ptr<const PPFHashMapSearch>;
84 
85  /** \brief Constructor for the PPFHashMapSearch class which sets the two step
86  * parameters for the enclosed data structure \param angle_discretization_step the
87  * step value between each bin of the hash map for the angular values \param
88  * distance_discretization_step the step value between each bin of the hash map for
89  * the distance values
90  */
91  PPFHashMapSearch(float angle_discretization_step = 12.0f / 180.0f *
92  static_cast<float>(M_PI),
93  float distance_discretization_step = 0.01f)
94  : feature_hash_map_(new FeatureHashMapType)
95  , internals_initialized_(false)
96  , angle_discretization_step_(angle_discretization_step)
97  , distance_discretization_step_(distance_discretization_step)
98  , max_dist_(-1.0f)
99  {}
100 
101  /** \brief Method that sets the feature cloud to be inserted in the hash map
102  * \param feature_cloud a const smart pointer to the PPFSignature feature cloud
103  */
104  void
106 
107  /** \brief Function for finding the nearest neighbors for the given feature inside the
108  * discretized hash map \param f1 The 1st value describing the query PPFSignature
109  * feature \param f2 The 2nd value describing the query PPFSignature feature \param f3
110  * The 3rd value describing the query PPFSignature feature \param f4 The 4th value
111  * describing the query PPFSignature feature \param indices a vector of pair indices
112  * representing the feature pairs that have been found in the bin corresponding to the
113  * query feature
114  */
115  void
117  float& f2,
118  float& f3,
119  float& f4,
120  std::vector<std::pair<std::size_t, std::size_t>>& indices);
121 
122  /** \brief Convenience method for returning a copy of the class instance as a
123  * shared_ptr */
124  Ptr
126  {
127  return Ptr(new PPFHashMapSearch(*this));
128  }
129 
130  /** \brief Returns the angle discretization step parameter (the step value between
131  * each bin of the hash map for the angular values) */
132  inline float
134  {
135  return angle_discretization_step_;
136  }
137 
138  /** \brief Returns the distance discretization step parameter (the step value between
139  * each bin of the hash map for the distance values) */
140  inline float
142  {
143  return distance_discretization_step_;
144  }
145 
146  /** \brief Returns the maximum distance found between any feature pair in the given
147  * input feature cloud */
148  inline float
150  {
151  return max_dist_;
152  }
153 
154  std::vector<std::vector<float>> alpha_m_;
155 
156 private:
157  FeatureHashMapTypePtr feature_hash_map_;
158  bool internals_initialized_;
159 
160  float angle_discretization_step_, distance_discretization_step_;
161  float max_dist_;
162 };
163 
164 /** \brief Class that registers two point clouds based on their sets of PPFSignatures.
165  * Please refer to the following publication for more details:
166  * B. Drost, M. Ulrich, N. Navab, S. Ilic
167  * Model Globally, Match Locally: Efficient and Robust 3D Object Recognition
168  * 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
169  * 13-18 June 2010, San Francisco, CA
170  *
171  * \note This class works in tandem with the PPFEstimation class
172  *
173  * \author Alexandru-Eugen Ichim
174  */
175 template <typename PointSource, typename PointTarget>
176 class PPFRegistration : public Registration<PointSource, PointTarget> {
177 public:
178  /** \brief Structure for storing a pose (represented as an Eigen::Affine3f) and an
179  * integer for counting votes \note initially used std::pair<Eigen::Affine3f, unsigned
180  * int>, but it proved problematic because of the Eigen structures alignment problems
181  * - std::pair does not have a custom allocator
182  */
183  struct PoseWithVotes {
184  PoseWithVotes(Eigen::Affine3f& a_pose, unsigned int& a_votes)
185  : pose(a_pose), votes(a_votes)
186  {}
187 
188  Eigen::Affine3f pose;
189  unsigned int votes;
190  };
192  std::vector<PoseWithVotes, Eigen::aligned_allocator<PoseWithVotes>>;
193 
194  /// input_ is the model cloud
196  /// target_ is the scene cloud
201 
205 
209 
210  /** \brief Empty constructor that initializes all the parameters of the algorithm with
211  * default values */
213  : Registration<PointSource, PointTarget>()
214  , scene_reference_point_sampling_rate_(5)
215  , clustering_position_diff_threshold_(0.01f)
216  , clustering_rotation_diff_threshold_(20.0f / 180.0f * static_cast<float>(M_PI))
217  {}
218 
219  /** \brief Method for setting the position difference clustering parameter
220  * \param clustering_position_diff_threshold distance threshold below which two poses
221  * are considered close enough to be in the same cluster (for the clustering phase of
222  * the algorithm)
223  */
224  inline void
225  setPositionClusteringThreshold(float clustering_position_diff_threshold)
226  {
227  clustering_position_diff_threshold_ = clustering_position_diff_threshold;
228  }
229 
230  /** \brief Returns the parameter defining the position difference clustering parameter
231  * - distance threshold below which two poses are considered close enough to be in the
232  * same cluster (for the clustering phase of the algorithm)
233  */
234  inline float
236  {
237  return clustering_position_diff_threshold_;
238  }
239 
240  /** \brief Method for setting the rotation clustering parameter
241  * \param clustering_rotation_diff_threshold rotation difference threshold below which
242  * two poses are considered to be in the same cluster (for the clustering phase of the
243  * algorithm)
244  */
245  inline void
246  setRotationClusteringThreshold(float clustering_rotation_diff_threshold)
247  {
248  clustering_rotation_diff_threshold_ = clustering_rotation_diff_threshold;
249  }
250 
251  /** \brief Returns the parameter defining the rotation clustering threshold
252  */
253  inline float
255  {
256  return clustering_rotation_diff_threshold_;
257  }
258 
259  /** \brief Method for setting the scene reference point sampling rate
260  * \param scene_reference_point_sampling_rate sampling rate for the scene reference
261  * point
262  */
263  inline void
264  setSceneReferencePointSamplingRate(unsigned int scene_reference_point_sampling_rate)
265  {
266  scene_reference_point_sampling_rate_ = scene_reference_point_sampling_rate;
267  }
268 
269  /** \brief Returns the parameter for the scene reference point sampling rate of the
270  * algorithm */
271  inline unsigned int
273  {
274  return scene_reference_point_sampling_rate_;
275  }
276 
277  /** \brief Function that sets the search method for the algorithm
278  * \note Right now, the only available method is the one initially proposed by
279  * the authors - by using a hash map with discretized feature vectors
280  * \param search_method smart pointer to the search method to be set
281  */
282  inline void
284  {
285  search_method_ = search_method;
286  }
287 
288  /** \brief Getter function for the search method of the class */
289  inline PPFHashMapSearch::Ptr
291  {
292  return search_method_;
293  }
294 
295  /** \brief Provide a pointer to the input target (e.g., the point cloud that we want
296  * to align the input source to) \param cloud the input point cloud target
297  */
298  void
299  setInputTarget(const PointCloudTargetConstPtr& cloud) override;
300 
301 private:
302  /** \brief Method that calculates the transformation between the input_ and target_
303  * point clouds, based on the PPF features */
304  void
305  computeTransformation(PointCloudSource& output,
306  const Eigen::Matrix4f& guess) override;
307 
308  /** \brief the search method that is going to be used to find matching feature pairs
309  */
310  PPFHashMapSearch::Ptr search_method_;
311 
312  /** \brief parameter for the sampling rate of the scene reference points */
313  uindex_t scene_reference_point_sampling_rate_;
314 
315  /** \brief position and rotation difference thresholds below which two
316  * poses are considered to be in the same cluster (for the clustering phase of the
317  * algorithm) */
318  float clustering_position_diff_threshold_, clustering_rotation_diff_threshold_;
319 
320  /** \brief use a kd-tree with range searches of range max_dist to skip an O(N) pass
321  * through the point cloud */
322  typename pcl::KdTreeFLANN<PointTarget>::Ptr scene_search_tree_;
323 
324  /** \brief static method used for the std::sort function to order two PoseWithVotes
325  * instances by their number of votes*/
326  static bool
327  poseWithVotesCompareFunction(const PoseWithVotes& a, const PoseWithVotes& b);
328 
329  /** \brief static method used for the std::sort function to order two pairs <index,
330  * votes> by the number of votes (unsigned integer value) */
331  static bool
332  clusterVotesCompareFunction(const std::pair<std::size_t, unsigned int>& a,
333  const std::pair<std::size_t, unsigned int>& b);
334 
335  /** \brief Method that clusters a set of given poses by using the clustering
336  * thresholds and their corresponding number of votes (see publication for more
337  * details) */
338  void
339  clusterPoses(PoseWithVotesList& poses, PoseWithVotesList& result);
340 
341  /** \brief Method that checks whether two poses are close together - based on the
342  * clustering threshold parameters of the class */
343  bool
344  posesWithinErrorBounds(Eigen::Affine3f& pose1, Eigen::Affine3f& pose2);
345 };
346 } // namespace pcl
347 
348 #include <pcl/registration/impl/ppf_registration.hpp>
shared_ptr< KdTreeFLANN< PointT, Dist > > Ptr
Definition: kdtree_flann.h:151
float getAngleDiscretizationStep() const
Returns the angle discretization step parameter (the step value between each bin of the hash map for ...
std::vector< std::vector< float > > alpha_m_
shared_ptr< FeatureHashMapType > FeatureHashMapTypePtr
std::unordered_multimap< HashKeyStruct, std::pair< std::size_t, std::size_t >, HashKeyStruct > FeatureHashMapType
shared_ptr< PPFHashMapSearch > Ptr
shared_ptr< const PPFHashMapSearch > ConstPtr
void nearestNeighborSearch(float &f1, float &f2, float &f3, float &f4, std::vector< std::pair< std::size_t, std::size_t >> &indices)
Function for finding the nearest neighbors for the given feature inside the discretized hash map.
Ptr makeShared()
Convenience method for returning a copy of the class instance as a shared_ptr.
PPFHashMapSearch(float angle_discretization_step=12.0f/180.0f *static_cast< float >(M_PI), float distance_discretization_step=0.01f)
Constructor for the PPFHashMapSearch class which sets the two step parameters for the enclosed data s...
float getDistanceDiscretizationStep() const
Returns the distance discretization step parameter (the step value between each bin of the hash map f...
void setInputFeatureCloud(PointCloud< PPFSignature >::ConstPtr feature_cloud)
Method that sets the feature cloud to be inserted in the hash map.
float getModelDiameter() const
Returns the maximum distance found between any feature pair in the given input feature cloud.
Class that registers two point clouds based on their sets of PPFSignatures.
typename PointCloudSource::Ptr PointCloudSourcePtr
unsigned int getSceneReferencePointSamplingRate()
Returns the parameter for the scene reference point sampling rate of the algorithm.
float getRotationClusteringThreshold()
Returns the parameter defining the rotation clustering threshold.
typename PointCloudTarget::Ptr PointCloudTargetPtr
void setRotationClusteringThreshold(float clustering_rotation_diff_threshold)
Method for setting the rotation clustering parameter.
PPFHashMapSearch::Ptr getSearchMethod()
Getter function for the search method of the class.
typename PointCloudSource::ConstPtr PointCloudSourceConstPtr
PPFRegistration()
Empty constructor that initializes all the parameters of the algorithm with default values.
pcl::PointCloud< PointSource > PointCloudSource
void setSceneReferencePointSamplingRate(unsigned int scene_reference_point_sampling_rate)
Method for setting the scene reference point sampling rate.
std::vector< PoseWithVotes, Eigen::aligned_allocator< PoseWithVotes > > PoseWithVotesList
float getPositionClusteringThreshold()
Returns the parameter defining the position difference clustering parameter.
typename PointCloudTarget::ConstPtr PointCloudTargetConstPtr
void setPositionClusteringThreshold(float clustering_position_diff_threshold)
Method for setting the position difference clustering parameter.
void setInputTarget(const PointCloudTargetConstPtr &cloud) override
Provide a pointer to the input target (e.g., the point cloud that we want to align the input source t...
void setSearchMethod(PPFHashMapSearch::Ptr search_method)
Function that sets the search method for the algorithm.
shared_ptr< PointCloud< PointSource > > Ptr
Definition: point_cloud.h:413
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:414
Registration represents the base registration class for general purpose, ICP-like methods.
Definition: registration.h:57
detail::int_type_t< detail::index_type_size, false > uindex_t
Type used for an unsigned index in PCL.
Definition: types.h:120
#define PCL_EXPORTS
Definition: pcl_macros.h:323
#define M_PI
Definition: pcl_macros.h:201
Data structure to hold the information for the key in the feature hash map of the PPFHashMapSearch cl...
HashKeyStruct(int a, int b, int c, int d)
std::size_t operator()(const HashKeyStruct &s) const noexcept
Structure for storing a pose (represented as an Eigen::Affine3f) and an integer for counting votes.
PoseWithVotes(Eigen::Affine3f &a_pose, unsigned int &a_votes)