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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  /// RS hash function https://www.partow.net/programming/hashfunctions/index.html
71  std::size_t b_ = 378551;
72  std::size_t a_ = 63689;
73  std::size_t hash = 0;
74  hash = hash * a_ + s.first;
75  a_ = a_ * b_;
76  hash = hash * a_ + s.second.first;
77  a_ = a_ * b_;
78  hash = hash * a_ + s.second.second.first;
79  a_ = a_ * b_;
80  hash = hash * a_ + s.second.second.second;
81  return hash;
82  }
83  };
85  std::unordered_multimap<HashKeyStruct,
86  std::pair<std::size_t, std::size_t>,
88  using FeatureHashMapTypePtr = shared_ptr<FeatureHashMapType>;
89  using Ptr = shared_ptr<PPFHashMapSearch>;
90  using ConstPtr = shared_ptr<const PPFHashMapSearch>;
91 
92  /** \brief Constructor for the PPFHashMapSearch class which sets the two step
93  * parameters for the enclosed data structure \param angle_discretization_step the
94  * step value between each bin of the hash map for the angular values \param
95  * distance_discretization_step the step value between each bin of the hash map for
96  * the distance values
97  */
98  PPFHashMapSearch(float angle_discretization_step = 12.0f / 180.0f *
99  static_cast<float>(M_PI),
100  float distance_discretization_step = 0.01f)
101  : feature_hash_map_(new FeatureHashMapType)
102  , angle_discretization_step_(angle_discretization_step)
103  , distance_discretization_step_(distance_discretization_step)
104  {}
105 
106  /** \brief Method that sets the feature cloud to be inserted in the hash map
107  * \param feature_cloud a const smart pointer to the PPFSignature feature cloud
108  */
109  void
111 
112  /** \brief Function for finding the nearest neighbors for the given feature inside the
113  * discretized hash map \param f1 The 1st value describing the query PPFSignature
114  * feature \param f2 The 2nd value describing the query PPFSignature feature \param f3
115  * The 3rd value describing the query PPFSignature feature \param f4 The 4th value
116  * describing the query PPFSignature feature \param indices a vector of pair indices
117  * representing the feature pairs that have been found in the bin corresponding to the
118  * query feature
119  */
120  void
122  float& f2,
123  float& f3,
124  float& f4,
125  std::vector<std::pair<std::size_t, std::size_t>>& indices);
126 
127  /** \brief Convenience method for returning a copy of the class instance as a
128  * shared_ptr */
129  Ptr
131  {
132  return Ptr(new PPFHashMapSearch(*this));
133  }
134 
135  /** \brief Returns the angle discretization step parameter (the step value between
136  * each bin of the hash map for the angular values) */
137  inline float
139  {
140  return angle_discretization_step_;
141  }
142 
143  /** \brief Returns the distance discretization step parameter (the step value between
144  * each bin of the hash map for the distance values) */
145  inline float
147  {
148  return distance_discretization_step_;
149  }
150 
151  /** \brief Returns the maximum distance found between any feature pair in the given
152  * input feature cloud */
153  inline float
155  {
156  return max_dist_;
157  }
158 
159  std::vector<std::vector<float>> alpha_m_;
160 
161 private:
162  FeatureHashMapTypePtr feature_hash_map_;
163  bool internals_initialized_{false};
164 
165  float angle_discretization_step_, distance_discretization_step_;
166  float max_dist_{-1.0f};
167 };
168 
169 /** \brief Class that registers two point clouds based on their sets of PPFSignatures.
170  * Please refer to the following publication for more details:
171  * B. Drost, M. Ulrich, N. Navab, S. Ilic
172  * Model Globally, Match Locally: Efficient and Robust 3D Object Recognition
173  * 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
174  * 13-18 June 2010, San Francisco, CA
175  *
176  * \note This class works in tandem with the PPFEstimation class
177  * \ingroup registration
178  *
179  * \author Alexandru-Eugen Ichim
180  */
181 template <typename PointSource, typename PointTarget>
182 class PPFRegistration : public Registration<PointSource, PointTarget> {
183 public:
184  /** \brief Structure for storing a pose (represented as an Eigen::Affine3f) and an
185  * integer for counting votes \note initially used std::pair<Eigen::Affine3f, unsigned
186  * int>, but it proved problematic because of the Eigen structures alignment problems
187  * - std::pair does not have a custom allocator
188  */
189  struct PoseWithVotes {
190  PoseWithVotes(const Eigen::Affine3f& a_pose, unsigned int& a_votes)
191  : pose(a_pose), votes(a_votes)
192  {}
193 
194  Eigen::Affine3f pose;
195  unsigned int votes;
196  };
198  std::vector<PoseWithVotes, Eigen::aligned_allocator<PoseWithVotes>>;
199 
200  /// input_ is the model cloud
202  /// target_ is the scene cloud
207 
211 
215 
216  /** \brief Empty constructor that initializes all the parameters of the algorithm with
217  * default values */
219  : Registration<PointSource, PointTarget>()
220  , clustering_rotation_diff_threshold_(20.0f / 180.0f * static_cast<float>(M_PI))
221  {}
222 
223  /** \brief Method for setting the position difference clustering parameter
224  * \param clustering_position_diff_threshold distance threshold below which two poses
225  * are considered close enough to be in the same cluster (for the clustering phase of
226  * the algorithm)
227  */
228  inline void
229  setPositionClusteringThreshold(float clustering_position_diff_threshold)
230  {
231  clustering_position_diff_threshold_ = clustering_position_diff_threshold;
232  }
233 
234  /** \brief Returns the parameter defining the position difference clustering parameter
235  * - distance threshold below which two poses are considered close enough to be in the
236  * same cluster (for the clustering phase of the algorithm)
237  */
238  inline float
240  {
241  return clustering_position_diff_threshold_;
242  }
243 
244  /** \brief Method for setting the rotation clustering parameter
245  * \param clustering_rotation_diff_threshold rotation difference threshold below which
246  * two poses are considered to be in the same cluster (for the clustering phase of the
247  * algorithm)
248  */
249  inline void
250  setRotationClusteringThreshold(float clustering_rotation_diff_threshold)
251  {
252  clustering_rotation_diff_threshold_ = clustering_rotation_diff_threshold;
253  }
254 
255  /** \brief Returns the parameter defining the rotation clustering threshold
256  */
257  inline float
259  {
260  return clustering_rotation_diff_threshold_;
261  }
262 
263  /** \brief Method for setting the scene reference point sampling rate
264  * \param scene_reference_point_sampling_rate sampling rate for the scene reference
265  * point
266  */
267  inline void
268  setSceneReferencePointSamplingRate(unsigned int scene_reference_point_sampling_rate)
269  {
270  scene_reference_point_sampling_rate_ = scene_reference_point_sampling_rate;
271  }
272 
273  /** \brief Returns the parameter for the scene reference point sampling rate of the
274  * algorithm */
275  inline unsigned int
277  {
278  return scene_reference_point_sampling_rate_;
279  }
280 
281  /** \brief Function that sets the search method for the algorithm
282  * \note Right now, the only available method is the one initially proposed by
283  * the authors - by using a hash map with discretized feature vectors
284  * \param search_method smart pointer to the search method to be set
285  */
286  inline void
288  {
289  search_method_ = search_method;
290  }
291 
292  /** \brief Getter function for the search method of the class */
293  inline PPFHashMapSearch::Ptr
295  {
296  return search_method_;
297  }
298 
299  /** \brief Provide a pointer to the input target (e.g., the point cloud that we want
300  * to align the input source to) \param cloud the input point cloud target
301  */
302  void
303  setInputTarget(const PointCloudTargetConstPtr& cloud) override;
304 
305  /** \brief Returns the most promising pose candidates, after clustering. The pose with
306  * the most votes is the result of the registration. It may make sense to check the
307  * next best pose candidates if the registration did not give the right result, or if
308  * there are more than one correct results. You need to call the align function before
309  * this one.
310  */
311  inline PoseWithVotesList
313  {
314  return best_pose_candidates;
315  }
316 
317 private:
318  /** \brief Method that calculates the transformation between the input_ and target_
319  * point clouds, based on the PPF features */
320  void
321  computeTransformation(PointCloudSource& output,
322  const Eigen::Matrix4f& guess) override;
323 
324  /** \brief the search method that is going to be used to find matching feature pairs
325  */
326  PPFHashMapSearch::Ptr search_method_;
327 
328  /** \brief parameter for the sampling rate of the scene reference points */
329  uindex_t scene_reference_point_sampling_rate_{5};
330 
331  /** \brief position and rotation difference thresholds below which two
332  * poses are considered to be in the same cluster (for the clustering phase of the
333  * algorithm) */
334  float clustering_position_diff_threshold_{0.01f}, clustering_rotation_diff_threshold_;
335 
336  /** \brief use a kd-tree with range searches of range max_dist to skip an O(N) pass
337  * through the point cloud */
338  typename pcl::KdTreeFLANN<PointTarget>::Ptr scene_search_tree_;
339 
340  /** \brief List with the most promising pose candidates, after clustering. The pose
341  * with the most votes is returned as the registration result. */
342  PoseWithVotesList best_pose_candidates;
343 
344  /** \brief static method used for the std::sort function to order two PoseWithVotes
345  * instances by their number of votes*/
346  static bool
347  poseWithVotesCompareFunction(const PoseWithVotes& a, const PoseWithVotes& b);
348 
349  /** \brief static method used for the std::sort function to order two pairs <index,
350  * votes> by the number of votes (unsigned integer value) */
351  static bool
352  clusterVotesCompareFunction(const std::pair<std::size_t, unsigned int>& a,
353  const std::pair<std::size_t, unsigned int>& b);
354 
355  /** \brief Method that clusters a set of given poses by using the clustering
356  * thresholds and their corresponding number of votes (see publication for more
357  * details) */
358  void
359  clusterPoses(PoseWithVotesList& poses, PoseWithVotesList& result);
360 
361  /** \brief Method that checks whether two poses are close together - based on the
362  * clustering threshold parameters of the class */
363  bool
364  posesWithinErrorBounds(Eigen::Affine3f& pose1,
365  Eigen::Affine3f& pose2,
366  float& position_diff,
367  float& rotation_diff_angle);
368 };
369 } // namespace pcl
370 
371 #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.
PoseWithVotesList getBestPoseCandidates()
Returns the most promising pose candidates, after clustering.
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:324
#define M_PI
Definition: pcl_macros.h:203
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(const Eigen::Affine3f &a_pose, unsigned int &a_votes)