Point Cloud Library (PCL)  1.12.1-dev
ppf_registration.hpp
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41 
42 #ifndef PCL_REGISTRATION_IMPL_PPF_REGISTRATION_H_
43 #define PCL_REGISTRATION_IMPL_PPF_REGISTRATION_H_
44 
45 #include <pcl/common/transforms.h>
46 #include <pcl/features/pfh.h>
47 #include <pcl/features/pfh_tools.h> // for computePairFeatures
48 #include <pcl/features/ppf.h>
49 #include <pcl/registration/ppf_registration.h>
50 //////////////////////////////////////////////////////////////////////////////////////////////
51 template <typename PointSource, typename PointTarget>
52 void
54  const PointCloudTargetConstPtr& cloud)
55 {
57 
58  scene_search_tree_ =
60  scene_search_tree_->setInputCloud(target_);
61 }
62 
63 //////////////////////////////////////////////////////////////////////////////////////////////
64 template <typename PointSource, typename PointTarget>
65 void
67  PointCloudSource& output, const Eigen::Matrix4f& guess)
68 {
69  if (!search_method_) {
70  PCL_ERROR("[pcl::PPFRegistration::computeTransformation] Search method not set - "
71  "skipping computeTransformation!\n");
72  return;
73  }
74 
75  if (guess != Eigen::Matrix4f::Identity()) {
76  PCL_ERROR("[pcl::PPFRegistration::computeTransformation] setting initial transform "
77  "(guess) not implemented!\n");
78  }
79 
80  const auto aux_size = static_cast<std::size_t>(
81  std::floor(2 * M_PI / search_method_->getAngleDiscretizationStep()));
82 
83  const std::vector<unsigned int> tmp_vec(aux_size, 0);
84  std::vector<std::vector<unsigned int>> accumulator_array(input_->size(), tmp_vec);
85 
86  PCL_INFO("Accumulator array size: %u x %u.\n",
87  accumulator_array.size(),
88  accumulator_array.back().size());
89 
90  PoseWithVotesList voted_poses;
91  // Consider every <scene_reference_point_sampling_rate>-th point as the reference
92  // point => fix s_r
93  float f1, f2, f3, f4;
94  for (index_t scene_reference_index = 0;
95  scene_reference_index < static_cast<index_t>(target_->size());
96  scene_reference_index += scene_reference_point_sampling_rate_) {
97  Eigen::Vector3f scene_reference_point =
98  (*target_)[scene_reference_index].getVector3fMap(),
99  scene_reference_normal =
100  (*target_)[scene_reference_index].getNormalVector3fMap();
101 
102  float rotation_angle_sg =
103  std::acos(scene_reference_normal.dot(Eigen::Vector3f::UnitX()));
104  bool parallel_to_x_sg =
105  (scene_reference_normal.y() == 0.0f && scene_reference_normal.z() == 0.0f);
106  Eigen::Vector3f rotation_axis_sg =
107  (parallel_to_x_sg)
108  ? (Eigen::Vector3f::UnitY())
109  : (scene_reference_normal.cross(Eigen::Vector3f::UnitX()).normalized());
110  Eigen::AngleAxisf rotation_sg(rotation_angle_sg, rotation_axis_sg);
111  Eigen::Affine3f transform_sg(
112  Eigen::Translation3f(rotation_sg * ((-1) * scene_reference_point)) *
113  rotation_sg);
114 
115  // For every other point in the scene => now have pair (s_r, s_i) fixed
116  pcl::Indices indices;
117  std::vector<float> distances;
118  scene_search_tree_->radiusSearch((*target_)[scene_reference_index],
119  search_method_->getModelDiameter() / 2,
120  indices,
121  distances);
122  for (const auto& scene_point_index : indices)
123  // for(std::size_t i = 0; i < target_->size (); ++i)
124  {
125  // size_t scene_point_index = i;
126  if (scene_reference_index != scene_point_index) {
127  if (/*pcl::computePPFPairFeature*/ pcl::computePairFeatures(
128  (*target_)[scene_reference_index].getVector4fMap(),
129  (*target_)[scene_reference_index].getNormalVector4fMap(),
130  (*target_)[scene_point_index].getVector4fMap(),
131  (*target_)[scene_point_index].getNormalVector4fMap(),
132  f1,
133  f2,
134  f3,
135  f4)) {
136  std::vector<std::pair<std::size_t, std::size_t>> nearest_indices;
137  search_method_->nearestNeighborSearch(f1, f2, f3, f4, nearest_indices);
138 
139  // Compute alpha_s angle
140  Eigen::Vector3f scene_point = (*target_)[scene_point_index].getVector3fMap();
141 
142  Eigen::Vector3f scene_point_transformed = transform_sg * scene_point;
143  float alpha_s =
144  std::atan2(-scene_point_transformed(2), scene_point_transformed(1));
145  if (std::sin(alpha_s) * scene_point_transformed(2) < 0.0f)
146  alpha_s *= (-1);
147  alpha_s *= (-1);
148 
149  // Go through point pairs in the model with the same discretized feature
150  for (const auto& nearest_index : nearest_indices) {
151  std::size_t model_reference_index = nearest_index.first;
152  std::size_t model_point_index = nearest_index.second;
153  // Calculate angle alpha = alpha_m - alpha_s
154  float alpha =
155  search_method_->alpha_m_[model_reference_index][model_point_index] -
156  alpha_s;
157  if (alpha < -M_PI) {
158  alpha += (2 * M_PI);
159  }
160  else if (alpha > M_PI) {
161  alpha -= (2 * M_PI);
162  }
163  unsigned int alpha_discretized = static_cast<unsigned int>(std::floor(
164  (alpha + M_PI) / search_method_->getAngleDiscretizationStep()));
165  accumulator_array[model_reference_index][alpha_discretized]++;
166  }
167  }
168  else
169  PCL_ERROR("[pcl::PPFRegistration::computeTransformation] Computing pair "
170  "feature vector between points %u and %u went wrong.\n",
171  scene_reference_index,
172  scene_point_index);
173  }
174  }
175 
176  std::size_t max_votes_i = 0, max_votes_j = 0;
177  unsigned int max_votes = 0;
178 
179  for (std::size_t i = 0; i < accumulator_array.size(); ++i)
180  for (std::size_t j = 0; j < accumulator_array.back().size(); ++j) {
181  if (accumulator_array[i][j] > max_votes) {
182  max_votes = accumulator_array[i][j];
183  max_votes_i = i;
184  max_votes_j = j;
185  }
186  // Reset accumulator_array for the next set of iterations with a new scene
187  // reference point
188  accumulator_array[i][j] = 0;
189  }
190 
191  Eigen::Vector3f model_reference_point = (*input_)[max_votes_i].getVector3fMap(),
192  model_reference_normal =
193  (*input_)[max_votes_i].getNormalVector3fMap();
194  float rotation_angle_mg =
195  std::acos(model_reference_normal.dot(Eigen::Vector3f::UnitX()));
196  bool parallel_to_x_mg =
197  (model_reference_normal.y() == 0.0f && model_reference_normal.z() == 0.0f);
198  Eigen::Vector3f rotation_axis_mg =
199  (parallel_to_x_mg)
200  ? (Eigen::Vector3f::UnitY())
201  : (model_reference_normal.cross(Eigen::Vector3f::UnitX()).normalized());
202  Eigen::AngleAxisf rotation_mg(rotation_angle_mg, rotation_axis_mg);
203  Eigen::Affine3f transform_mg(
204  Eigen::Translation3f(rotation_mg * ((-1) * model_reference_point)) *
205  rotation_mg);
206  Eigen::Affine3f max_transform =
207  transform_sg.inverse() *
208  Eigen::AngleAxisf((static_cast<float>(max_votes_j + 0.5) *
209  search_method_->getAngleDiscretizationStep() -
210  M_PI),
211  Eigen::Vector3f::UnitX()) *
212  transform_mg;
213 
214  voted_poses.push_back(PoseWithVotes(max_transform, max_votes));
215  }
216  PCL_DEBUG("Done with the Hough Transform ...\n");
217 
218  // Cluster poses for filtering out outliers and obtaining more precise results
219  PoseWithVotesList results;
220  clusterPoses(voted_poses, results);
221 
222  pcl::transformPointCloud(*input_, output, results.front().pose);
223 
224  transformation_ = final_transformation_ = results.front().pose.matrix();
225  converged_ = true;
226 }
227 
228 //////////////////////////////////////////////////////////////////////////////////////////////
229 template <typename PointSource, typename PointTarget>
230 void
234 {
235  PCL_INFO("Clustering poses ...\n");
236  // Start off by sorting the poses by the number of votes
237  sort(poses.begin(), poses.end(), poseWithVotesCompareFunction);
238 
239  std::vector<PoseWithVotesList> clusters;
240  std::vector<std::pair<std::size_t, unsigned int>> cluster_votes;
241  for (std::size_t poses_i = 0; poses_i < poses.size(); ++poses_i) {
242  bool found_cluster = false;
243  for (std::size_t clusters_i = 0; clusters_i < clusters.size(); ++clusters_i) {
244  if (posesWithinErrorBounds(poses[poses_i].pose,
245  clusters[clusters_i].front().pose)) {
246  found_cluster = true;
247  clusters[clusters_i].push_back(poses[poses_i]);
248  cluster_votes[clusters_i].second += poses[poses_i].votes;
249  break;
250  }
251  }
252 
253  if (!found_cluster) {
254  // Create a new cluster with the current pose
255  PoseWithVotesList new_cluster;
256  new_cluster.push_back(poses[poses_i]);
257  clusters.push_back(new_cluster);
258  cluster_votes.push_back(std::pair<std::size_t, unsigned int>(
259  clusters.size() - 1, poses[poses_i].votes));
260  }
261  }
262 
263  // Sort clusters by total number of votes
264  std::sort(cluster_votes.begin(), cluster_votes.end(), clusterVotesCompareFunction);
265  // Compute pose average and put them in result vector
266  /// @todo some kind of threshold for determining whether a cluster has enough votes or
267  /// not... now just taking the first three clusters
268  result.clear();
269  std::size_t max_clusters = (clusters.size() < 3) ? clusters.size() : 3;
270  for (std::size_t cluster_i = 0; cluster_i < max_clusters; ++cluster_i) {
271  PCL_INFO("Winning cluster has #votes: %d and #poses voted: %d.\n",
272  cluster_votes[cluster_i].second,
273  clusters[cluster_votes[cluster_i].first].size());
274  Eigen::Vector3f translation_average(0.0, 0.0, 0.0);
275  Eigen::Vector4f rotation_average(0.0, 0.0, 0.0, 0.0);
276  for (typename PoseWithVotesList::iterator v_it =
277  clusters[cluster_votes[cluster_i].first].begin();
278  v_it != clusters[cluster_votes[cluster_i].first].end();
279  ++v_it) {
280  translation_average += v_it->pose.translation();
281  /// averaging rotations by just averaging the quaternions in 4D space - reference
282  /// "On Averaging Rotations" by CLAUS GRAMKOW
283  rotation_average += Eigen::Quaternionf(v_it->pose.rotation()).coeffs();
284  }
285 
286  translation_average /=
287  static_cast<float>(clusters[cluster_votes[cluster_i].first].size());
288  rotation_average /=
289  static_cast<float>(clusters[cluster_votes[cluster_i].first].size());
290 
291  Eigen::Affine3f transform_average;
292  transform_average.translation().matrix() = translation_average;
293  transform_average.linear().matrix() =
294  Eigen::Quaternionf(rotation_average).normalized().toRotationMatrix();
295 
296  result.push_back(PoseWithVotes(transform_average, cluster_votes[cluster_i].second));
297  }
298 }
299 
300 //////////////////////////////////////////////////////////////////////////////////////////////
301 template <typename PointSource, typename PointTarget>
302 bool
304  Eigen::Affine3f& pose1, Eigen::Affine3f& pose2)
305 {
306  float position_diff = (pose1.translation() - pose2.translation()).norm();
307  Eigen::AngleAxisf rotation_diff_mat(
308  (pose1.rotation().inverse().lazyProduct(pose2.rotation()).eval()));
309 
310  float rotation_diff_angle = std::abs(rotation_diff_mat.angle());
311 
312  return (position_diff < clustering_position_diff_threshold_ &&
313  rotation_diff_angle < clustering_rotation_diff_threshold_);
314 }
315 
316 //////////////////////////////////////////////////////////////////////////////////////////////
317 template <typename PointSource, typename PointTarget>
318 bool
322 {
323  return (a.votes > b.votes);
324 }
325 
326 //////////////////////////////////////////////////////////////////////////////////////////////
327 template <typename PointSource, typename PointTarget>
328 bool
330  const std::pair<std::size_t, unsigned int>& a,
331  const std::pair<std::size_t, unsigned int>& b)
332 {
333  return (a.second > b.second);
334 }
335 
336 //#define PCL_INSTANTIATE_PPFRegistration(PointSource,PointTarget) template class
337 // PCL_EXPORTS pcl::PPFRegistration<PointSource, PointTarget>;
338 
339 #endif // PCL_REGISTRATION_IMPL_PPF_REGISTRATION_H_
shared_ptr< KdTreeFLANN< PointT, Dist > > Ptr
Definition: kdtree_flann.h:151
Class that registers two point clouds based on their sets of PPFSignatures.
std::vector< PoseWithVotes, Eigen::aligned_allocator< PoseWithVotes > > PoseWithVotesList
typename PointCloudTarget::ConstPtr PointCloudTargetConstPtr
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...
Registration represents the base registration class for general purpose, ICP-like methods.
Definition: registration.h:57
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
PCL_EXPORTS bool computePairFeatures(const Eigen::Vector4f &p1, const Eigen::Vector4f &n1, const Eigen::Vector4f &p2, const Eigen::Vector4f &n2, float &f1, float &f2, float &f3, float &f4)
Compute the 4-tuple representation containing the three angles and one distance between two points re...
__device__ __forceinline__ float3 normalized(const float3 &v)
Definition: utils.hpp:101
__device__ __host__ __forceinline__ float norm(const float3 &v1, const float3 &v2)
detail::int_type_t< detail::index_type_size, detail::index_type_signed > index_t
Type used for an index in PCL.
Definition: types.h:112
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
#define M_PI
Definition: pcl_macros.h:201
Structure for storing a pose (represented as an Eigen::Affine3f) and an integer for counting votes.