Point Cloud Library (PCL) 1.13.1
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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
73 using PointCloudSourcePtr = typename PointCloudSource::Ptr;
74 using PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr;
75
77 using PointCloudTargetPtr = typename PointCloudTarget::Ptr;
78 using PointCloudTargetConstPtr = typename PointCloudTarget::ConstPtr;
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.
92 * \param[in] original_correspondences the set of initial correspondences given
93 * \param[out] remaining_correspondences the resultant filtered set of remaining
94 * correspondences
95 */
96 void
97 getRemainingCorrespondences(const pcl::Correspondences& original_correspondences,
98 pcl::Correspondences& remaining_correspondences) override;
99
100 /** \brief Provide a source point cloud dataset (must contain XYZ data!), used to
101 * compute the correspondence distance.
102 * \param[in] cloud a cloud containing XYZ data
103 */
104 inline void
106 {
107 input_ = cloud;
108 }
109
110 /** \brief Provide a target point cloud dataset (must contain XYZ data!), used to
111 * compute the correspondence distance.
112 * \param[in] target a cloud containing XYZ data
113 */
114 inline void
116 {
117 target_ = target;
118 }
119
120 /** \brief See if this rejector requires source points */
121 bool
122 requiresSourcePoints() const override
123 {
124 return (true);
125 }
126
127 /** \brief Blob method for setting the source cloud */
128 void
130 {
132 fromPCLPointCloud2(*cloud2, *cloud);
133 setInputSource(cloud);
134 }
135
136 /** \brief See if this rejector requires a target cloud */
137 bool
138 requiresTargetPoints() const override
139 {
140 return (true);
141 }
142
143 /** \brief Method for setting the target cloud */
144 void
146 {
148 fromPCLPointCloud2(*cloud2, *cloud);
149 setInputTarget(cloud);
150 }
151
152 /** \brief Set the polygon cardinality
153 * \param cardinality polygon cardinality
154 */
155 inline void
156 setCardinality(int cardinality)
157 {
158 cardinality_ = cardinality;
159 }
160
161 /** \brief Get the polygon cardinality
162 * \return polygon cardinality
163 */
164 inline int
166 {
167 return (cardinality_);
168 }
169
170 /** \brief Set the similarity threshold in [0,1[ between edge lengths,
171 * where 1 is a perfect match
172 * \param similarity_threshold similarity threshold
173 */
174 inline void
175 setSimilarityThreshold(float similarity_threshold)
176 {
177 similarity_threshold_ = similarity_threshold;
178 similarity_threshold_squared_ = similarity_threshold * similarity_threshold;
179 }
180
181 /** \brief Get the similarity threshold between edge lengths
182 * \return similarity threshold
183 */
184 inline float
186 {
187 return (similarity_threshold_);
188 }
189
190 /** \brief Set the number of iterations
191 * \param iterations number of iterations
192 */
193 inline void
194 setIterations(int iterations)
195 {
196 iterations_ = iterations;
197 }
198
199 /** \brief Get the number of iterations
200 * \return number of iterations
201 */
202 inline int
204 {
205 return (iterations_);
206 }
207
208 /** \brief Polygonal rejection of a single polygon, indexed by a subset of
209 * correspondences \param corr all correspondences into \ref input_ and \ref target_
210 * \param idx sampled indices into \b correspondences, must have a size equal to \ref
211 * cardinality_ \return true if all edge length ratios are larger than or equal to
212 * \ref similarity_threshold_
213 */
214 inline bool
215 thresholdPolygon(const pcl::Correspondences& corr, const std::vector<int>& idx)
216 {
217 if (cardinality_ ==
218 2) // Special case: when two points are considered, we only have one edge
219 {
220 return (thresholdEdgeLength(corr[idx[0]].index_query,
221 corr[idx[1]].index_query,
222 corr[idx[0]].index_match,
223 corr[idx[1]].index_match,
224 similarity_threshold_squared_));
225 }
226 // Otherwise check all edges
227 for (int i = 0; i < cardinality_; ++i) {
228 if (!thresholdEdgeLength(corr[idx[i]].index_query,
229 corr[idx[(i + 1) % cardinality_]].index_query,
230 corr[idx[i]].index_match,
231 corr[idx[(i + 1) % cardinality_]].index_match,
232 similarity_threshold_squared_)) {
233 return (false);
234 }
235 }
236 return (true);
237 }
238
239 /** \brief Polygonal rejection of a single polygon, indexed by two point index vectors
240 * \param source_indices indices of polygon points in \ref input_, must have a size
241 * equal to \ref cardinality_
242 * \param target_indices corresponding indices of polygon points in \ref target_, must
243 * have a size equal to \ref cardinality_
244 * \return true if all edge length ratios are larger than or equal to
245 * \ref similarity_threshold_
246 */
247 inline bool
248 thresholdPolygon(const pcl::Indices& source_indices,
249 const pcl::Indices& target_indices)
250 {
251 // Convert indices to correspondences and an index vector pointing to each element
252 pcl::Correspondences corr(cardinality_);
253 std::vector<int> idx(cardinality_);
254 for (int i = 0; i < cardinality_; ++i) {
255 corr[i].index_query = source_indices[i];
256 corr[i].index_match = target_indices[i];
257 idx[i] = i;
258 }
259
260 return (thresholdPolygon(corr, idx));
261 }
262
263protected:
264 /** \brief Apply the rejection algorithm.
265 * \param[out] correspondences the set of resultant correspondences.
266 */
267 inline void
268 applyRejection(pcl::Correspondences& correspondences) override
269 {
270 getRemainingCorrespondences(*input_correspondences_, correspondences);
271 }
272
273 /** \brief Get k unique random indices in range {0,...,n-1} (sampling without
274 * replacement) \note No check is made to ensure that k <= n.
275 * \param n upper index range, exclusive
276 * \param k number of unique indices to sample
277 * \return k unique random indices in range {0,...,n-1}
278 */
279 inline std::vector<int>
281 {
282 // Marked sampled indices and sample counter
283 std::vector<bool> sampled(n, false);
284 int samples = 0;
285 // Resulting unique indices
286 std::vector<int> result;
287 result.reserve(k);
288 do {
289 // Pick a random index in the range
290 const int idx = (std::rand() % n);
291 // If unique
292 if (!sampled[idx]) {
293 // Mark as sampled and increment result counter
294 sampled[idx] = true;
295 ++samples;
296 // Store
297 result.push_back(idx);
298 }
299 } while (samples < k);
300
301 return (result);
302 }
303
304 /** \brief Squared Euclidean distance between two points using the members x, y and z
305 * \param p1 first point
306 * \param p2 second point
307 * \return squared Euclidean distance
308 */
309 inline float
310 computeSquaredDistance(const SourceT& p1, const TargetT& p2)
311 {
312 const float dx = p2.x - p1.x;
313 const float dy = p2.y - p1.y;
314 const float dz = p2.z - p1.z;
315
316 return (dx * dx + dy * dy + dz * dz);
317 }
318
319 /** \brief Edge length similarity thresholding
320 * \param index_query_1 index of first source vertex
321 * \param index_query_2 index of second source vertex
322 * \param index_match_1 index of first target vertex
323 * \param index_match_2 index of second target vertex
324 * \param simsq squared similarity threshold in [0,1]
325 * \return true if edge length ratio is larger than or equal to threshold
326 */
327 inline bool
328 thresholdEdgeLength(int index_query_1,
329 int index_query_2,
330 int index_match_1,
331 int index_match_2,
332 float simsq)
333 {
334 // Distance between source points
335 const float dist_src =
336 computeSquaredDistance((*input_)[index_query_1], (*input_)[index_query_2]);
337 // Distance between target points
338 const float dist_tgt =
339 computeSquaredDistance((*target_)[index_match_1], (*target_)[index_match_2]);
340 // Edge length similarity [0,1] where 1 is a perfect match
341 const float edge_sim =
342 (dist_src < dist_tgt ? dist_src / dist_tgt : dist_tgt / dist_src);
343
344 return (edge_sim >= simsq);
345 }
346
347 /** \brief Compute a linear histogram. This function is equivalent to the MATLAB
348 * function \b histc, with the edges set as follows: <b>
349 * lower:(upper-lower)/bins:upper </b>
350 * \param data input samples
351 * \param lower lower bound of input samples
352 * \param upper upper bound of input samples
353 * \param bins number of bins in output
354 * \return linear histogram
355 */
356 std::vector<int>
357 computeHistogram(const std::vector<float>& data, float lower, float upper, int bins);
358
359 /** \brief Find the optimal value for binary histogram thresholding using Otsu's
360 * method
361 * \param histogram input histogram \return threshold value according to Otsu's
362 * criterion
363 */
364 int
365 findThresholdOtsu(const std::vector<int>& histogram);
366
367 /** \brief The input point cloud dataset */
369
370 /** \brief The input point cloud dataset target */
372
373 /** \brief Number of iterations to run */
375
376 /** \brief The polygon cardinality used during rejection */
378
379 /** \brief Lower edge length threshold in [0,1] used for verifying polygon
380 * similarities, where 1 is a perfect match */
382
383 /** \brief Squared value if \ref similarity_threshold_, only for internal use */
385};
386} // namespace registration
387} // namespace pcl
388
389#include <pcl/registration/impl/correspondence_rejection_poly.hpp>
PointCloud represents the base class in PCL for storing collections of 3D points.
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.
shared_ptr< const CorrespondenceRejectorPoly< SourceT, TargetT > > ConstPtr
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.
shared_ptr< CorrespondenceRejectorPoly< SourceT, TargetT > > Ptr
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.
bool thresholdEdgeLength(int index_query_1, int index_query_2, int index_match_1, int index_match_2, float simsq)
Edge length similarity thresholding.
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.
void fromPCLPointCloud2(const pcl::PCLPointCloud2 &msg, pcl::PointCloud< PointT > &cloud, const MsgFieldMap &field_map, const std::uint8_t *msg_data)
Convert a PCLPointCloud2 binary data blob into a pcl::PointCloud<T> object using a field_map.
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
shared_ptr< const ::pcl::PCLPointCloud2 > ConstPtr