Point Cloud Library (PCL) 1.14.0
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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
46namespace pcl {
47
48namespace registration {
49
50template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
51bool
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
66template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
67void
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 PointTarget pt;
85 // Iterate over the input set of source indices
86 for (const auto& idx_i : (*indices_)) {
87 // Check if the template types are the same. If true, avoid a copy.
88 // Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT
89 // macro!
90 tree_->nearestKSearch(
91 detail::pointCopyOrRef<PointTarget, PointSource>(input_, idx_i),
92 k_,
93 nn_indices,
94 nn_dists);
95
96 // Among the K nearest neighbours find the one with minimum perpendicular distance
97 // to the normal
98 double min_dist = std::numeric_limits<double>::max();
99
100 // Find the best correspondence
101 for (std::size_t j = 0; j < nn_indices.size(); j++) {
102 // computing the distance between a point and a line in 3d.
103 // Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
104 pt.x = (*target_)[nn_indices[j]].x - (*input_)[idx_i].x;
105 pt.y = (*target_)[nn_indices[j]].y - (*input_)[idx_i].y;
106 pt.z = (*target_)[nn_indices[j]].z - (*input_)[idx_i].z;
107
108 const NormalT& normal = (*source_normals_)[idx_i];
109 Eigen::Vector3d N(normal.normal_x, normal.normal_y, normal.normal_z);
110 Eigen::Vector3d V(pt.x, pt.y, pt.z);
111 Eigen::Vector3d C = N.cross(V);
112
113 // Check if we have a better correspondence
114 double dist = C.dot(C);
115 if (dist < min_dist) {
116 min_dist = dist;
117 min_index = static_cast<int>(j);
118 }
119 }
120 if (min_dist > max_distance)
121 continue;
122
123 corr.index_query = idx_i;
124 corr.index_match = nn_indices[min_index];
125 corr.distance = nn_dists[min_index]; // min_dist;
126 correspondences[nr_valid_correspondences++] = corr;
127 }
128 correspondences.resize(nr_valid_correspondences);
129 deinitCompute();
130}
131
132template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
133void
136 double max_distance)
137{
138 if (!initCompute())
139 return;
140
141 // setup tree for reciprocal search
142 // Set the internal point representation of choice
143 if (!initComputeReciprocal())
144 return;
145
146 correspondences.resize(indices_->size());
147
148 pcl::Indices nn_indices(k_);
149 std::vector<float> nn_dists(k_);
150 pcl::Indices index_reciprocal(1);
151 std::vector<float> distance_reciprocal(1);
152
153 int min_index = 0;
154
156 unsigned int nr_valid_correspondences = 0;
157 int target_idx = 0;
158
159 PointTarget pt;
160 // Iterate over the input set of source indices
161 for (const auto& idx_i : (*indices_)) {
162 // Check if the template types are the same. If true, avoid a copy.
163 // Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT
164 // macro!
165 tree_->nearestKSearch(
166 detail::pointCopyOrRef<PointTarget, PointSource>(input_, idx_i),
167 k_,
168 nn_indices,
169 nn_dists);
170
171 // Among the K nearest neighbours find the one with minimum perpendicular distance
172 // to the normal
173 double min_dist = std::numeric_limits<double>::max();
174
175 // Find the best correspondence
176 for (std::size_t j = 0; j < nn_indices.size(); j++) {
177 // computing the distance between a point and a line in 3d.
178 // Reference - http://mathworld.wolfram.com/Point-LineDistance3-Dimensional.html
179 pt.x = (*target_)[nn_indices[j]].x - (*input_)[idx_i].x;
180 pt.y = (*target_)[nn_indices[j]].y - (*input_)[idx_i].y;
181 pt.z = (*target_)[nn_indices[j]].z - (*input_)[idx_i].z;
182
183 const NormalT& normal = (*source_normals_)[idx_i];
184 Eigen::Vector3d N(normal.normal_x, normal.normal_y, normal.normal_z);
185 Eigen::Vector3d V(pt.x, pt.y, pt.z);
186 Eigen::Vector3d C = N.cross(V);
187
188 // Check if we have a better correspondence
189 double dist = C.dot(C);
190 if (dist < min_dist) {
191 min_dist = dist;
192 min_index = static_cast<int>(j);
193 }
194 }
195 if (min_dist > max_distance)
196 continue;
197
198 // Check if the correspondence is reciprocal
199 target_idx = nn_indices[min_index];
200 tree_reciprocal_->nearestKSearch(
201 detail::pointCopyOrRef<PointSource, PointTarget>(target_, target_idx),
202 1,
203 index_reciprocal,
204 distance_reciprocal);
205
206 if (idx_i != index_reciprocal[0])
207 continue;
208
209 // Correspondence IS reciprocal, save it and continue
210 corr.index_query = idx_i;
211 corr.index_match = nn_indices[min_index];
212 corr.distance = nn_dists[min_index]; // min_dist;
213 correspondences[nr_valid_correspondences++] = corr;
214 }
215 correspondences.resize(nr_valid_correspondences);
216 deinitCompute();
217}
218
219} // namespace registration
220} // namespace pcl
221
222#endif // PCL_REGISTRATION_IMPL_CORRESPONDENCE_ESTIMATION_NORMAL_SHOOTING_H_
Abstract CorrespondenceEstimationBase class.
void determineReciprocalCorrespondences(pcl::Correspondences &correspondences, double max_distance=std::numeric_limits< double >::max()) override
Determine the reciprocal correspondences between input and target cloud.
void determineCorrespondences(pcl::Correspondences &correspondences, double max_distance=std::numeric_limits< double >::max()) override
Determine the correspondences between input and target cloud.
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
Correspondence represents a match between two entities (e.g., points, descriptors,...
index_t index_query
Index of the query (source) point.
index_t index_match
Index of the matching (target) point.
A point structure representing normal coordinates and the surface curvature estimate.