Point Cloud Library (PCL) 1.12.0
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correspondence_rejection_poly.h
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38
39#pragma once
40
41#include <pcl/registration/correspondence_rejection.h>
42#include <pcl/point_cloud.h>
43
44namespace pcl {
45namespace registration {
46/** \brief CorrespondenceRejectorPoly implements a correspondence rejection method that
47 * exploits low-level and pose-invariant geometric constraints between two point sets by
48 * forming virtual polygons of a user-specifiable cardinality on each model using the
49 * input correspondences. These polygons are then checked in a pose-invariant manner
50 * (i.e. the side lengths must be approximately equal), and rejection is performed by
51 * thresholding these edge lengths.
52 *
53 * If you use this in academic work, please cite:
54 *
55 * A. G. Buch, D. Kraft, J.-K. Kämäräinen, H. G. Petersen and N. Krüger.
56 * Pose Estimation using Local Structure-Specific Shape and Appearance Context.
57 * International Conference on Robotics and Automation (ICRA), 2013.
58 *
59 * \author Anders Glent Buch
60 * \ingroup registration
61 */
62template <typename SourceT, typename TargetT>
64 using CorrespondenceRejector::getClassName;
65 using CorrespondenceRejector::input_correspondences_;
66 using CorrespondenceRejector::rejection_name_;
67
68public:
69 using Ptr = shared_ptr<CorrespondenceRejectorPoly<SourceT, TargetT>>;
70 using ConstPtr = shared_ptr<const CorrespondenceRejectorPoly<SourceT, TargetT>>;
71
75
79
80 /** \brief Empty constructor */
82 : iterations_(10000)
83 , cardinality_(3)
84 , similarity_threshold_(0.75f)
85 , similarity_threshold_squared_(0.75f * 0.75f)
86 {
87 rejection_name_ = "CorrespondenceRejectorPoly";
88 }
89
90 /** \brief Get a list of valid correspondences after rejection from the original set
91 * of correspondences. \param[in] original_correspondences the set of initial
92 * correspondences given \param[out] remaining_correspondences the resultant filtered
93 * set of remaining correspondences
94 */
95 void
96 getRemainingCorrespondences(const pcl::Correspondences& original_correspondences,
97 pcl::Correspondences& remaining_correspondences) override;
98
99 /** \brief Provide a source point cloud dataset (must contain XYZ data!), used to
100 * compute the correspondence distance. \param[in] cloud a cloud containing XYZ data
101 */
102 inline void
104 {
105 input_ = cloud;
106 }
107
108 /** \brief Provide a target point cloud dataset (must contain XYZ data!), used to
109 * compute the correspondence distance. \param[in] target a cloud containing XYZ data
110 */
111 inline void
113 {
114 target_ = target;
115 }
116
117 /** \brief See if this rejector requires source points */
118 bool
119 requiresSourcePoints() const override
120 {
121 return (true);
122 }
123
124 /** \brief Blob method for setting the source cloud */
125 void
127 {
129 fromPCLPointCloud2(*cloud2, *cloud);
130 setInputSource(cloud);
131 }
132
133 /** \brief See if this rejector requires a target cloud */
134 bool
135 requiresTargetPoints() const override
136 {
137 return (true);
138 }
139
140 /** \brief Method for setting the target cloud */
141 void
143 {
145 fromPCLPointCloud2(*cloud2, *cloud);
146 setInputTarget(cloud);
147 }
148
149 /** \brief Set the polygon cardinality
150 * \param cardinality polygon cardinality
151 */
152 inline void
153 setCardinality(int cardinality)
154 {
155 cardinality_ = cardinality;
156 }
157
158 /** \brief Get the polygon cardinality
159 * \return polygon cardinality
160 */
161 inline int
163 {
164 return (cardinality_);
165 }
166
167 /** \brief Set the similarity threshold in [0,1[ between edge lengths,
168 * where 1 is a perfect match
169 * \param similarity_threshold similarity threshold
170 */
171 inline void
172 setSimilarityThreshold(float similarity_threshold)
173 {
174 similarity_threshold_ = similarity_threshold;
175 similarity_threshold_squared_ = similarity_threshold * similarity_threshold;
176 }
177
178 /** \brief Get the similarity threshold between edge lengths
179 * \return similarity threshold
180 */
181 inline float
183 {
184 return (similarity_threshold_);
185 }
186
187 /** \brief Set the number of iterations
188 * \param iterations number of iterations
189 */
190 inline void
191 setIterations(int iterations)
192 {
193 iterations_ = iterations;
194 }
195
196 /** \brief Get the number of iterations
197 * \return number of iterations
198 */
199 inline int
201 {
202 return (iterations_);
203 }
204
205 /** \brief Polygonal rejection of a single polygon, indexed by a subset of
206 * correspondences \param corr all correspondences into \ref input_ and \ref target_
207 * \param idx sampled indices into \b correspondences, must have a size equal to \ref
208 * cardinality_ \return true if all edge length ratios are larger than or equal to
209 * \ref similarity_threshold_
210 */
211 inline bool
212 thresholdPolygon(const pcl::Correspondences& corr, const std::vector<int>& idx)
213 {
214 if (cardinality_ ==
215 2) // Special case: when two points are considered, we only have one edge
216 {
217 return (thresholdEdgeLength(corr[idx[0]].index_query,
218 corr[idx[1]].index_query,
219 corr[idx[0]].index_match,
220 corr[idx[1]].index_match,
221 cardinality_));
222 }
223 // Otherwise check all edges
224 for (int i = 0; i < cardinality_; ++i) {
225 if (!thresholdEdgeLength(corr[idx[i]].index_query,
226 corr[idx[(i + 1) % cardinality_]].index_query,
227 corr[idx[i]].index_match,
228 corr[idx[(i + 1) % cardinality_]].index_match,
229 similarity_threshold_squared_)) {
230 return (false);
231 }
232 }
233 return (true);
234 }
235
236 /** \brief Polygonal rejection of a single polygon, indexed by two point index vectors
237 * \param source_indices indices of polygon points in \ref input_, must have a size
238 * equal to \ref cardinality_ \param target_indices corresponding indices of polygon
239 * points in \ref target_, must have a size equal to \ref cardinality_ \return true if
240 * all edge length ratios are larger than or equal to \ref similarity_threshold_
241 */
242 inline bool
243 thresholdPolygon(const pcl::Indices& source_indices,
244 const pcl::Indices& target_indices)
245 {
246 // Convert indices to correspondences and an index vector pointing to each element
247 pcl::Correspondences corr(cardinality_);
248 std::vector<int> idx(cardinality_);
249 for (int i = 0; i < cardinality_; ++i) {
250 corr[i].index_query = source_indices[i];
251 corr[i].index_match = target_indices[i];
252 idx[i] = i;
253 }
254
255 return (thresholdPolygon(corr, idx));
256 }
257
258protected:
259 /** \brief Apply the rejection algorithm.
260 * \param[out] correspondences the set of resultant correspondences.
261 */
262 inline void
263 applyRejection(pcl::Correspondences& correspondences) override
264 {
265 getRemainingCorrespondences(*input_correspondences_, correspondences);
266 }
267
268 /** \brief Get k unique random indices in range {0,...,n-1} (sampling without
269 * replacement) \note No check is made to ensure that k <= n. \param n upper index
270 * range, exclusive \param k number of unique indices to sample \return k unique
271 * random indices in range {0,...,n-1}
272 */
273 inline std::vector<int>
275 {
276 // Marked sampled indices and sample counter
277 std::vector<bool> sampled(n, false);
278 int samples = 0;
279 // Resulting unique indices
280 std::vector<int> result;
281 result.reserve(k);
282 do {
283 // Pick a random index in the range
284 const int idx = (std::rand() % n);
285 // If unique
286 if (!sampled[idx]) {
287 // Mark as sampled and increment result counter
288 sampled[idx] = true;
289 ++samples;
290 // Store
291 result.push_back(idx);
292 }
293 } while (samples < k);
294
295 return (result);
296 }
297
298 /** \brief Squared Euclidean distance between two points using the members x, y and z
299 * \param p1 first point
300 * \param p2 second point
301 * \return squared Euclidean distance
302 */
303 inline float
304 computeSquaredDistance(const SourceT& p1, const TargetT& p2)
305 {
306 const float dx = p2.x - p1.x;
307 const float dy = p2.y - p1.y;
308 const float dz = p2.z - p1.z;
309
310 return (dx * dx + dy * dy + dz * dz);
311 }
312
313 /** \brief Edge length similarity thresholding
314 * \param index_query_1 index of first source vertex
315 * \param index_query_2 index of second source vertex
316 * \param index_match_1 index of first target vertex
317 * \param index_match_2 index of second target vertex
318 * \param simsq squared similarity threshold in [0,1]
319 * \return true if edge length ratio is larger than or equal to threshold
320 */
321 inline bool
322 thresholdEdgeLength(int index_query_1,
323 int index_query_2,
324 int index_match_1,
325 int index_match_2,
326 float simsq)
327 {
328 // Distance between source points
329 const float dist_src =
330 computeSquaredDistance((*input_)[index_query_1], (*input_)[index_query_2]);
331 // Distance between target points
332 const float dist_tgt =
333 computeSquaredDistance((*target_)[index_match_1], (*target_)[index_match_2]);
334 // Edge length similarity [0,1] where 1 is a perfect match
335 const float edge_sim =
336 (dist_src < dist_tgt ? dist_src / dist_tgt : dist_tgt / dist_src);
337
338 return (edge_sim >= simsq);
339 }
340
341 /** \brief Compute a linear histogram. This function is equivalent to the MATLAB
342 * function \b histc, with the edges set as follows: <b>
343 * lower:(upper-lower)/bins:upper </b> \param data input samples \param lower lower
344 * bound of input samples \param upper upper bound of input samples \param bins number
345 * of bins in output \return linear histogram
346 */
347 std::vector<int>
348 computeHistogram(const std::vector<float>& data, float lower, float upper, int bins);
349
350 /** \brief Find the optimal value for binary histogram thresholding using Otsu's
351 * method \param histogram input histogram \return threshold value according to Otsu's
352 * criterion
353 */
354 int
355 findThresholdOtsu(const std::vector<int>& histogram);
356
357 /** \brief The input point cloud dataset */
359
360 /** \brief The input point cloud dataset target */
362
363 /** \brief Number of iterations to run */
365
366 /** \brief The polygon cardinality used during rejection */
368
369 /** \brief Lower edge length threshold in [0,1] used for verifying polygon
370 * similarities, where 1 is a perfect match */
372
373 /** \brief Squared value if \ref similarity_threshold_, only for internal use */
375};
376} // namespace registration
377} // namespace pcl
378
379#include <pcl/registration/impl/correspondence_rejection_poly.hpp>
PointCloud represents the base class in PCL for storing collections of 3D points.
shared_ptr< PointCloud< PointT > > Ptr
shared_ptr< const PointCloud< PointT > > ConstPtr
CorrespondenceRejector represents the base class for correspondence rejection methods
CorrespondenceRejectorPoly implements a correspondence rejection method that exploits low-level and p...
float similarity_threshold_squared_
Squared value if similarity_threshold_, only for internal use.
std::vector< int > getUniqueRandomIndices(int n, int k)
Get k unique random indices in range {0,...,n-1} (sampling without replacement)
bool thresholdPolygon(const pcl::Correspondences &corr, const std::vector< int > &idx)
Polygonal rejection of a single polygon, indexed by a subset of correspondences.
float similarity_threshold_
Lower edge length threshold in [0,1] used for verifying polygon similarities, where 1 is a perfect ma...
float computeSquaredDistance(const SourceT &p1, const TargetT &p2)
Squared Euclidean distance between two points using the members x, y and z.
typename PointCloudTarget::ConstPtr PointCloudTargetConstPtr
PointCloudSourceConstPtr input_
The input point cloud dataset.
float getSimilarityThreshold()
Get the similarity threshold between edge lengths.
void setTargetPoints(pcl::PCLPointCloud2::ConstPtr cloud2) override
Method for setting the target cloud.
bool thresholdPolygon(const pcl::Indices &source_indices, const pcl::Indices &target_indices)
Polygonal rejection of a single polygon, indexed by two point index vectors.
void applyRejection(pcl::Correspondences &correspondences) override
Apply the rejection algorithm.
void setCardinality(int cardinality)
Set the polygon cardinality.
void setSimilarityThreshold(float similarity_threshold)
Set the similarity threshold in [0,1[ between edge lengths, where 1 is a perfect match.
void setIterations(int iterations)
Set the number of iterations.
shared_ptr< CorrespondenceRejectorPoly< SourceT, TargetT > > Ptr
bool thresholdEdgeLength(int index_query_1, int index_query_2, int index_match_1, int index_match_2, float simsq)
Edge length similarity thresholding.
shared_ptr< const CorrespondenceRejectorPoly< SourceT, TargetT > > ConstPtr
PointCloudTargetConstPtr target_
The input point cloud dataset target.
void setInputTarget(const PointCloudTargetConstPtr &target)
Provide a target point cloud dataset (must contain XYZ data!), used to compute the correspondence dis...
void setSourcePoints(pcl::PCLPointCloud2::ConstPtr cloud2) override
Blob method for setting the source cloud.
void setInputSource(const PointCloudSourceConstPtr &cloud)
Provide a source point cloud dataset (must contain XYZ data!), used to compute the correspondence dis...
bool requiresTargetPoints() const override
See if this rejector requires a target cloud.
bool requiresSourcePoints() const override
See if this rejector requires source points.
typename PointCloudSource::ConstPtr PointCloudSourceConstPtr
int cardinality_
The polygon cardinality used during rejection.
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
void fromPCLPointCloud2(const pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud, const MsgFieldMap &field_map)
Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.
shared_ptr< const ::pcl::PCLPointCloud2 > ConstPtr