Point Cloud Library (PCL)  1.11.1-dev
correspondence_estimation_normal_shooting.hpp
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40 
41 #ifndef PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_NORMAL_SHOOTING_H_
42 #define PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_NORMAL_SHOOTING_H_
43 
44 #include <pcl/common/copy_point.h>
45 
46 namespace pcl {
47 
48 namespace registration {
49 
50 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
51 bool
54 {
55  if (!source_normals_) {
56  PCL_WARN("[pcl::registration::%s::initCompute] Datasets containing normals for "
57  "source have not been given!\n",
58  getClassName().c_str());
59  return (false);
60  }
61 
62  return (
64 }
65 
66 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
67 void
69  determineCorrespondences(pcl::Correspondences& correspondences, double max_distance)
70 {
71  if (!initCompute())
72  return;
73 
74  correspondences.resize(indices_->size());
75 
76  pcl::Indices nn_indices(k_);
77  std::vector<float> nn_dists(k_);
78 
79  int min_index = 0;
80 
82  unsigned int nr_valid_correspondences = 0;
83 
84  // Check if the template types are the same. If true, avoid a copy.
85  // Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT
86  // macro!
87  if (isSamePointType<PointSource, PointTarget>()) {
88  PointTarget pt;
89  // Iterate over the input set of source indices
90  for (const auto& idx_i : (*indices_)) {
91  tree_->nearestKSearch((*input_)[idx_i], k_, nn_indices, nn_dists);
92 
93  // Among the K nearest neighbours find the one with minimum perpendicular distance
94  // to the normal
95  double min_dist = std::numeric_limits<double>::max();
96 
97  // Find the best correspondence
98  for (std::size_t j = 0; j < nn_indices.size(); j++) {
99  // computing the distance between a point and a line in 3d.
100  // Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
101  pt.x = (*target_)[nn_indices[j]].x - (*input_)[idx_i].x;
102  pt.y = (*target_)[nn_indices[j]].y - (*input_)[idx_i].y;
103  pt.z = (*target_)[nn_indices[j]].z - (*input_)[idx_i].z;
104 
105  const NormalT& normal = (*source_normals_)[idx_i];
106  Eigen::Vector3d N(normal.normal_x, normal.normal_y, normal.normal_z);
107  Eigen::Vector3d V(pt.x, pt.y, pt.z);
108  Eigen::Vector3d C = N.cross(V);
109 
110  // Check if we have a better correspondence
111  double dist = C.dot(C);
112  if (dist < min_dist) {
113  min_dist = dist;
114  min_index = static_cast<int>(j);
115  }
116  }
117  if (min_dist > max_distance)
118  continue;
119 
120  corr.index_query = idx_i;
121  corr.index_match = nn_indices[min_index];
122  corr.distance = nn_dists[min_index]; // min_dist;
123  correspondences[nr_valid_correspondences++] = corr;
124  }
125  }
126  else {
127  PointTarget pt;
128 
129  // Iterate over the input set of source indices
130  for (const auto& idx_i : (*indices_)) {
131  tree_->nearestKSearch((*input_)[idx_i], k_, nn_indices, nn_dists);
132 
133  // Among the K nearest neighbours find the one with minimum perpendicular distance
134  // to the normal
135  double min_dist = std::numeric_limits<double>::max();
136 
137  // Find the best correspondence
138  for (std::size_t j = 0; j < nn_indices.size(); j++) {
139  PointSource pt_src;
140  // Copy the source data to a target PointTarget format so we can search in the
141  // tree
142  copyPoint((*input_)[idx_i], pt_src);
143 
144  // computing the distance between a point and a line in 3d.
145  // Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
146  pt.x = (*target_)[nn_indices[j]].x - pt_src.x;
147  pt.y = (*target_)[nn_indices[j]].y - pt_src.y;
148  pt.z = (*target_)[nn_indices[j]].z - pt_src.z;
149 
150  const NormalT& normal = (*source_normals_)[idx_i];
151  Eigen::Vector3d N(normal.normal_x, normal.normal_y, normal.normal_z);
152  Eigen::Vector3d V(pt.x, pt.y, pt.z);
153  Eigen::Vector3d C = N.cross(V);
154 
155  // Check if we have a better correspondence
156  double dist = C.dot(C);
157  if (dist < min_dist) {
158  min_dist = dist;
159  min_index = static_cast<int>(j);
160  }
161  }
162  if (min_dist > max_distance)
163  continue;
164 
165  corr.index_query = idx_i;
166  corr.index_match = nn_indices[min_index];
167  corr.distance = nn_dists[min_index]; // min_dist;
168  correspondences[nr_valid_correspondences++] = corr;
169  }
170  }
171  correspondences.resize(nr_valid_correspondences);
172  deinitCompute();
173 }
174 
175 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
176 void
179  double max_distance)
180 {
181  if (!initCompute())
182  return;
183 
184  // setup tree for reciprocal search
185  // Set the internal point representation of choice
186  if (!initComputeReciprocal())
187  return;
188 
189  correspondences.resize(indices_->size());
190 
191  pcl::Indices nn_indices(k_);
192  std::vector<float> nn_dists(k_);
193  pcl::Indices index_reciprocal(1);
194  std::vector<float> distance_reciprocal(1);
195 
196  int min_index = 0;
197 
198  pcl::Correspondence corr;
199  unsigned int nr_valid_correspondences = 0;
200  int target_idx = 0;
201 
202  // Check if the template types are the same. If true, avoid a copy.
203  // Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT
204  // macro!
205  if (isSamePointType<PointSource, PointTarget>()) {
206  PointTarget pt;
207  // Iterate over the input set of source indices
208  for (const auto& idx_i : (*indices_)) {
209  tree_->nearestKSearch((*input_)[idx_i], k_, nn_indices, nn_dists);
210 
211  // Among the K nearest neighbours find the one with minimum perpendicular distance
212  // to the normal
213  double min_dist = std::numeric_limits<double>::max();
214 
215  // Find the best correspondence
216  for (std::size_t j = 0; j < nn_indices.size(); j++) {
217  // computing the distance between a point and a line in 3d.
218  // Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
219  pt.x = (*target_)[nn_indices[j]].x - (*input_)[idx_i].x;
220  pt.y = (*target_)[nn_indices[j]].y - (*input_)[idx_i].y;
221  pt.z = (*target_)[nn_indices[j]].z - (*input_)[idx_i].z;
222 
223  const NormalT& normal = (*source_normals_)[idx_i];
224  Eigen::Vector3d N(normal.normal_x, normal.normal_y, normal.normal_z);
225  Eigen::Vector3d V(pt.x, pt.y, pt.z);
226  Eigen::Vector3d C = N.cross(V);
227 
228  // Check if we have a better correspondence
229  double dist = C.dot(C);
230  if (dist < min_dist) {
231  min_dist = dist;
232  min_index = static_cast<int>(j);
233  }
234  }
235  if (min_dist > max_distance)
236  continue;
237 
238  // Check if the correspondence is reciprocal
239  target_idx = nn_indices[min_index];
240  tree_reciprocal_->nearestKSearch(
241  (*target_)[target_idx], 1, index_reciprocal, distance_reciprocal);
242 
243  if (idx_i != index_reciprocal[0])
244  continue;
245 
246  // Correspondence IS reciprocal, save it and continue
247  corr.index_query = idx_i;
248  corr.index_match = nn_indices[min_index];
249  corr.distance = nn_dists[min_index]; // min_dist;
250  correspondences[nr_valid_correspondences++] = corr;
251  }
252  }
253  else {
254  PointTarget pt;
255 
256  // Iterate over the input set of source indices
257  for (const auto& idx_i : (*indices_)) {
258  tree_->nearestKSearch((*input_)[idx_i], k_, nn_indices, nn_dists);
259 
260  // Among the K nearest neighbours find the one with minimum perpendicular distance
261  // to the normal
262  double min_dist = std::numeric_limits<double>::max();
263 
264  // Find the best correspondence
265  for (std::size_t j = 0; j < nn_indices.size(); j++) {
266  PointSource pt_src;
267  // Copy the source data to a target PointTarget format so we can search in the
268  // tree
269  copyPoint((*input_)[idx_i], pt_src);
270 
271  // computing the distance between a point and a line in 3d.
272  // Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
273  pt.x = (*target_)[nn_indices[j]].x - pt_src.x;
274  pt.y = (*target_)[nn_indices[j]].y - pt_src.y;
275  pt.z = (*target_)[nn_indices[j]].z - pt_src.z;
276 
277  const NormalT& normal = (*source_normals_)[idx_i];
278  Eigen::Vector3d N(normal.normal_x, normal.normal_y, normal.normal_z);
279  Eigen::Vector3d V(pt.x, pt.y, pt.z);
280  Eigen::Vector3d C = N.cross(V);
281 
282  // Check if we have a better correspondence
283  double dist = C.dot(C);
284  if (dist < min_dist) {
285  min_dist = dist;
286  min_index = static_cast<int>(j);
287  }
288  }
289  if (min_dist > max_distance)
290  continue;
291 
292  // Check if the correspondence is reciprocal
293  target_idx = nn_indices[min_index];
294  tree_reciprocal_->nearestKSearch(
295  (*target_)[target_idx], 1, index_reciprocal, distance_reciprocal);
296 
297  if (idx_i != index_reciprocal[0])
298  continue;
299 
300  // Correspondence IS reciprocal, save it and continue
301  corr.index_query = idx_i;
302  corr.index_match = nn_indices[min_index];
303  corr.distance = nn_dists[min_index]; // min_dist;
304  correspondences[nr_valid_correspondences++] = corr;
305  }
306  }
307  correspondences.resize(nr_valid_correspondences);
308  deinitCompute();
309 }
310 
311 } // namespace registration
312 } // namespace pcl
313 
314 #endif // PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_NORMAL_SHOOTING_H_
pcl
Definition: convolution.h:46
pcl::Normal
A point structure representing normal coordinates and the surface curvature estimate.
Definition: point_types.hpp:855
pcl::registration::CorrespondenceEstimationNormalShooting::determineCorrespondences
void determineCorrespondences(pcl::Correspondences &correspondences, double max_distance=std::numeric_limits< double >::max()) override
Determine the correspondences between input and target cloud.
Definition: correspondence_estimation_normal_shooting.hpp:69
pcl::Correspondence::distance
float distance
Definition: correspondence.h:69
pcl::registration::CorrespondenceEstimationNormalShooting::determineReciprocalCorrespondences
void determineReciprocalCorrespondences(pcl::Correspondences &correspondences, double max_distance=std::numeric_limits< double >::max()) override
Determine the reciprocal correspondences between input and target cloud.
Definition: correspondence_estimation_normal_shooting.hpp:178
pcl::registration::CorrespondenceEstimationBase
Abstract CorrespondenceEstimationBase class.
Definition: correspondence_estimation.h:60
pcl::copyPoint
void copyPoint(const PointInT &point_in, PointOutT &point_out)
Copy the fields of a source point into a target point.
Definition: copy_point.hpp:137
pcl::registration::CorrespondenceEstimationNormalShooting::initCompute
bool initCompute()
Internal computation initialization.
Definition: correspondence_estimation_normal_shooting.hpp:53
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
pcl::Correspondence::index_match
index_t index_match
Index of the matching (target) point.
Definition: correspondence.h:65
pcl::Correspondences
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
Definition: correspondence.h:89
pcl::Correspondence::index_query
index_t index_query
Index of the query (source) point.
Definition: correspondence.h:63
pcl::Correspondence
Correspondence represents a match between two entities (e.g., points, descriptors,...
Definition: correspondence.h:60