Point Cloud Library (PCL) 1.12.0
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sac_model_plane.hpp
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40
41#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_PLANE_H_
42#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_PLANE_H_
43
44#include <pcl/sample_consensus/sac_model_plane.h>
45#include <pcl/common/centroid.h>
46#include <pcl/common/eigen.h>
47#include <pcl/common/concatenate.h>
48
49//////////////////////////////////////////////////////////////////////////
50template <typename PointT> bool
52{
53 if (samples.size () != sample_size_)
54 {
55 PCL_ERROR ("[pcl::SampleConsensusModelPlane::isSampleGood] Wrong number of samples (is %lu, should be %lu)!\n", samples.size (), sample_size_);
56 return (false);
57 }
58 // Get the values at the two points
59 pcl::Array4fMapConst p0 = (*input_)[samples[0]].getArray4fMap ();
60 pcl::Array4fMapConst p1 = (*input_)[samples[1]].getArray4fMap ();
61 pcl::Array4fMapConst p2 = (*input_)[samples[2]].getArray4fMap ();
62
63 Eigen::Array4f dy1dy2 = (p1-p0) / (p2-p0);
64
65 return ( (dy1dy2[0] != dy1dy2[1]) || (dy1dy2[2] != dy1dy2[1]) );
66}
67
68//////////////////////////////////////////////////////////////////////////
69template <typename PointT> bool
71 const Indices &samples, Eigen::VectorXf &model_coefficients) const
72{
73 // Need 3 samples
74 if (samples.size () != sample_size_)
75 {
76 PCL_ERROR ("[pcl::SampleConsensusModelPlane::computeModelCoefficients] Invalid set of samples given (%lu)!\n", samples.size ());
77 return (false);
78 }
79
80 pcl::Array4fMapConst p0 = (*input_)[samples[0]].getArray4fMap ();
81 pcl::Array4fMapConst p1 = (*input_)[samples[1]].getArray4fMap ();
82 pcl::Array4fMapConst p2 = (*input_)[samples[2]].getArray4fMap ();
83
84 // Compute the segment values (in 3d) between p1 and p0
85 Eigen::Array4f p1p0 = p1 - p0;
86 // Compute the segment values (in 3d) between p2 and p0
87 Eigen::Array4f p2p0 = p2 - p0;
88
89 // Avoid some crashes by checking for collinearity here
90 Eigen::Array4f dy1dy2 = p1p0 / p2p0;
91 if ( (dy1dy2[0] == dy1dy2[1]) && (dy1dy2[2] == dy1dy2[1]) ) // Check for collinearity
92 {
93 return (false);
94 }
95
96 // Compute the plane coefficients from the 3 given points in a straightforward manner
97 // calculate the plane normal n = (p2-p1) x (p3-p1) = cross (p2-p1, p3-p1)
98 model_coefficients.resize (model_size_);
99 model_coefficients[0] = p1p0[1] * p2p0[2] - p1p0[2] * p2p0[1];
100 model_coefficients[1] = p1p0[2] * p2p0[0] - p1p0[0] * p2p0[2];
101 model_coefficients[2] = p1p0[0] * p2p0[1] - p1p0[1] * p2p0[0];
102 model_coefficients[3] = 0.0f;
103
104 // Normalize
105 model_coefficients.normalize ();
106
107 // ... + d = 0
108 model_coefficients[3] = -1.0f * (model_coefficients.template head<4>().dot (p0.matrix ()));
109
110 PCL_DEBUG ("[pcl::SampleConsensusModelPlane::computeModelCoefficients] Model is (%g,%g,%g,%g).\n",
111 model_coefficients[0], model_coefficients[1], model_coefficients[2], model_coefficients[3]);
112 return (true);
113}
114
115#define AT(POS) ((*input_)[(*indices_)[(POS)]])
116
117#ifdef __AVX__
118// This function computes the distances of 8 points to the plane
119template <typename PointT> inline __m256 pcl::SampleConsensusModelPlane<PointT>::dist8 (const std::size_t i, const __m256 &a_vec, const __m256 &b_vec, const __m256 &c_vec, const __m256 &d_vec, const __m256 &abs_help) const
120{
121 // The andnot-function realizes an abs-operation: the sign bit is removed
122 return _mm256_andnot_ps (abs_help,
123 _mm256_add_ps (_mm256_add_ps (_mm256_mul_ps (a_vec, _mm256_set_ps (AT(i ).x, AT(i+1).x, AT(i+2).x, AT(i+3).x, AT(i+4).x, AT(i+5).x, AT(i+6).x, AT(i+7).x)),
124 _mm256_mul_ps (b_vec, _mm256_set_ps (AT(i ).y, AT(i+1).y, AT(i+2).y, AT(i+3).y, AT(i+4).y, AT(i+5).y, AT(i+6).y, AT(i+7).y))),
125 _mm256_add_ps (_mm256_mul_ps (c_vec, _mm256_set_ps (AT(i ).z, AT(i+1).z, AT(i+2).z, AT(i+3).z, AT(i+4).z, AT(i+5).z, AT(i+6).z, AT(i+7).z)),
126 d_vec))); // TODO this could be replaced by three fmadd-instructions (if available), but the speed gain would probably be minimal
127}
128#endif // ifdef __AVX__
129
130#ifdef __SSE__
131// This function computes the distances of 4 points to the plane
132template <typename PointT> inline __m128 pcl::SampleConsensusModelPlane<PointT>::dist4 (const std::size_t i, const __m128 &a_vec, const __m128 &b_vec, const __m128 &c_vec, const __m128 &d_vec, const __m128 &abs_help) const
133{
134 // The andnot-function realizes an abs-operation: the sign bit is removed
135 return _mm_andnot_ps (abs_help,
136 _mm_add_ps (_mm_add_ps (_mm_mul_ps (a_vec, _mm_set_ps (AT(i ).x, AT(i+1).x, AT(i+2).x, AT(i+3).x)),
137 _mm_mul_ps (b_vec, _mm_set_ps (AT(i ).y, AT(i+1).y, AT(i+2).y, AT(i+3).y))),
138 _mm_add_ps (_mm_mul_ps (c_vec, _mm_set_ps (AT(i ).z, AT(i+1).z, AT(i+2).z, AT(i+3).z)),
139 d_vec))); // TODO this could be replaced by three fmadd-instructions (if available), but the speed gain would probably be minimal
140}
141#endif // ifdef __SSE__
142
143#undef AT
144
145//////////////////////////////////////////////////////////////////////////
146template <typename PointT> void
148 const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
149{
150 // Needs a valid set of model coefficients
151 if (!isModelValid (model_coefficients))
152 {
153 PCL_ERROR ("[pcl::SampleConsensusModelPlane::getDistancesToModel] Given model is invalid!\n");
154 return;
155 }
156
157 distances.resize (indices_->size ());
158
159 // Iterate through the 3d points and calculate the distances from them to the plane
160 for (std::size_t i = 0; i < indices_->size (); ++i)
161 {
162 // Calculate the distance from the point to the plane normal as the dot product
163 // D = (P-A).N/|N|
164 /*distances[i] = std::abs (model_coefficients[0] * (*input_)[(*indices_)[i]].x +
165 model_coefficients[1] * (*input_)[(*indices_)[i]].y +
166 model_coefficients[2] * (*input_)[(*indices_)[i]].z +
167 model_coefficients[3]);*/
168 Eigen::Vector4f pt ((*input_)[(*indices_)[i]].x,
169 (*input_)[(*indices_)[i]].y,
170 (*input_)[(*indices_)[i]].z,
171 1.0f);
172 distances[i] = std::abs (model_coefficients.dot (pt));
173 }
174}
175
176//////////////////////////////////////////////////////////////////////////
177template <typename PointT> void
179 const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
180{
181 // Needs a valid set of model coefficients
182 if (!isModelValid (model_coefficients))
183 {
184 PCL_ERROR ("[pcl::SampleConsensusModelPlane::selectWithinDistance] Given model is invalid!\n");
185 return;
186 }
187
188 inliers.clear ();
189 error_sqr_dists_.clear ();
190 inliers.reserve (indices_->size ());
191 error_sqr_dists_.reserve (indices_->size ());
192
193 // Iterate through the 3d points and calculate the distances from them to the plane
194 for (std::size_t i = 0; i < indices_->size (); ++i)
195 {
196 // Calculate the distance from the point to the plane normal as the dot product
197 // D = (P-A).N/|N|
198 Eigen::Vector4f pt ((*input_)[(*indices_)[i]].x,
199 (*input_)[(*indices_)[i]].y,
200 (*input_)[(*indices_)[i]].z,
201 1.0f);
202
203 float distance = std::abs (model_coefficients.dot (pt));
204
205 if (distance < threshold)
206 {
207 // Returns the indices of the points whose distances are smaller than the threshold
208 inliers.push_back ((*indices_)[i]);
209 error_sqr_dists_.push_back (static_cast<double> (distance));
210 }
211 }
212}
213
214//////////////////////////////////////////////////////////////////////////
215template <typename PointT> std::size_t
217 const Eigen::VectorXf &model_coefficients, const double threshold) const
218{
219 // Needs a valid set of model coefficients
220 if (!isModelValid (model_coefficients))
221 {
222 PCL_ERROR ("[pcl::SampleConsensusModelPlane::countWithinDistance] Given model is invalid!\n");
223 return (0);
224 }
225#if defined (__AVX__) && defined (__AVX2__)
226 return countWithinDistanceAVX (model_coefficients, threshold);
227#elif defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
228 return countWithinDistanceSSE (model_coefficients, threshold);
229#else
230 return countWithinDistanceStandard (model_coefficients, threshold);
231#endif
232}
233
234//////////////////////////////////////////////////////////////////////////
235template <typename PointT> std::size_t
237 const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i) const
238{
239 std::size_t nr_p = 0;
240 // Iterate through the 3d points and calculate the distances from them to the plane
241 for (; i < indices_->size (); ++i)
242 {
243 // Calculate the distance from the point to the plane normal as the dot product
244 // D = (P-A).N/|N|
245 Eigen::Vector4f pt ((*input_)[(*indices_)[i]].x,
246 (*input_)[(*indices_)[i]].y,
247 (*input_)[(*indices_)[i]].z,
248 1.0f);
249 if (std::abs (model_coefficients.dot (pt)) < threshold)
250 {
251 nr_p++;
252 }
253 }
254 return (nr_p);
255}
256
257//////////////////////////////////////////////////////////////////////////
258#if defined (__SSE__) && defined (__SSE2__) && defined (__SSE4_1__)
259template <typename PointT> std::size_t
261 const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i) const
262{
263 std::size_t nr_p = 0;
264 const __m128 a_vec = _mm_set1_ps (model_coefficients[0]);
265 const __m128 b_vec = _mm_set1_ps (model_coefficients[1]);
266 const __m128 c_vec = _mm_set1_ps (model_coefficients[2]);
267 const __m128 d_vec = _mm_set1_ps (model_coefficients[3]);
268 const __m128 threshold_vec = _mm_set1_ps (threshold);
269 const __m128 abs_help = _mm_set1_ps (-0.0F); // -0.0F (negative zero) means that all bits are 0, only the sign bit is 1
270 __m128i res = _mm_set1_epi32(0); // This corresponds to nr_p: 4 32bit integers that, summed together, hold the number of inliers
271 for (; (i + 4) <= indices_->size (); i += 4)
272 {
273 const __m128 mask = _mm_cmplt_ps (dist4 (i, a_vec, b_vec, c_vec, d_vec, abs_help), threshold_vec); // The mask contains 1 bits if the corresponding points are inliers, else 0 bits
274 res = _mm_add_epi32 (res, _mm_and_si128 (_mm_set1_epi32 (1), _mm_castps_si128 (mask))); // The latter part creates a vector with ones (as 32bit integers) where the points are inliers
275 //const int res = _mm_movemask_ps (mask);
276 //if (res & 1) nr_p++;
277 //if (res & 2) nr_p++;
278 //if (res & 4) nr_p++;
279 //if (res & 8) nr_p++;
280 }
281 nr_p += _mm_extract_epi32 (res, 0);
282 nr_p += _mm_extract_epi32 (res, 1);
283 nr_p += _mm_extract_epi32 (res, 2);
284 nr_p += _mm_extract_epi32 (res, 3);
285
286 // Process the remaining points (at most 3)
287 nr_p += countWithinDistanceStandard(model_coefficients, threshold, i);
288 return (nr_p);
289}
290#endif
291
292//////////////////////////////////////////////////////////////////////////
293#if defined (__AVX__) && defined (__AVX2__)
294template <typename PointT> std::size_t
296 const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i) const
297{
298 std::size_t nr_p = 0;
299 const __m256 a_vec = _mm256_set1_ps (model_coefficients[0]);
300 const __m256 b_vec = _mm256_set1_ps (model_coefficients[1]);
301 const __m256 c_vec = _mm256_set1_ps (model_coefficients[2]);
302 const __m256 d_vec = _mm256_set1_ps (model_coefficients[3]);
303 const __m256 threshold_vec = _mm256_set1_ps (threshold);
304 const __m256 abs_help = _mm256_set1_ps (-0.0F); // -0.0F (negative zero) means that all bits are 0, only the sign bit is 1
305 __m256i res = _mm256_set1_epi32(0); // This corresponds to nr_p: 8 32bit integers that, summed together, hold the number of inliers
306 for (; (i + 8) <= indices_->size (); i += 8)
307 {
308 const __m256 mask = _mm256_cmp_ps (dist8 (i, a_vec, b_vec, c_vec, d_vec, abs_help), threshold_vec, _CMP_LT_OQ); // The mask contains 1 bits if the corresponding points are inliers, else 0 bits
309 res = _mm256_add_epi32 (res, _mm256_and_si256 (_mm256_set1_epi32 (1), _mm256_castps_si256 (mask))); // The latter part creates a vector with ones (as 32bit integers) where the points are inliers
310 //const int res = _mm256_movemask_ps (mask);
311 //if (res & 1) nr_p++;
312 //if (res & 2) nr_p++;
313 //if (res & 4) nr_p++;
314 //if (res & 8) nr_p++;
315 //if (res & 16) nr_p++;
316 //if (res & 32) nr_p++;
317 //if (res & 64) nr_p++;
318 //if (res & 128) nr_p++;
319 }
320 nr_p += _mm256_extract_epi32 (res, 0);
321 nr_p += _mm256_extract_epi32 (res, 1);
322 nr_p += _mm256_extract_epi32 (res, 2);
323 nr_p += _mm256_extract_epi32 (res, 3);
324 nr_p += _mm256_extract_epi32 (res, 4);
325 nr_p += _mm256_extract_epi32 (res, 5);
326 nr_p += _mm256_extract_epi32 (res, 6);
327 nr_p += _mm256_extract_epi32 (res, 7);
328
329 // Process the remaining points (at most 7)
330 nr_p += countWithinDistanceStandard(model_coefficients, threshold, i);
331 return (nr_p);
332}
333#endif
334
335//////////////////////////////////////////////////////////////////////////
336template <typename PointT> void
338 const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const
339{
340 // Needs a valid set of model coefficients
341 if (!isModelValid (model_coefficients))
342 {
343 PCL_ERROR ("[pcl::SampleConsensusModelPlane::optimizeModelCoefficients] Given model is invalid!\n");
344 optimized_coefficients = model_coefficients;
345 return;
346 }
347
348 // Need more than the minimum sample size to make a difference
349 if (inliers.size () <= sample_size_)
350 {
351 PCL_ERROR ("[pcl::SampleConsensusModelPlane::optimizeModelCoefficients] Not enough inliers found to optimize model coefficients (%lu)! Returning the same coefficients.\n", inliers.size ());
352 optimized_coefficients = model_coefficients;
353 return;
354 }
355
356 Eigen::Vector4f plane_parameters;
357
358 // Use Least-Squares to fit the plane through all the given sample points and find out its coefficients
359 EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
360 Eigen::Vector4f xyz_centroid;
361
362 computeMeanAndCovarianceMatrix (*input_, inliers, covariance_matrix, xyz_centroid);
363
364 // Compute the model coefficients
365 EIGEN_ALIGN16 Eigen::Vector3f::Scalar eigen_value;
366 EIGEN_ALIGN16 Eigen::Vector3f eigen_vector;
367 pcl::eigen33 (covariance_matrix, eigen_value, eigen_vector);
368
369 // Hessian form (D = nc . p_plane (centroid here) + p)
370 optimized_coefficients.resize (model_size_);
371 optimized_coefficients[0] = eigen_vector [0];
372 optimized_coefficients[1] = eigen_vector [1];
373 optimized_coefficients[2] = eigen_vector [2];
374 optimized_coefficients[3] = 0.0f;
375 optimized_coefficients[3] = -1.0f * optimized_coefficients.dot (xyz_centroid);
376
377 // Make sure it results in a valid model
378 if (!isModelValid (optimized_coefficients))
379 {
380 optimized_coefficients = model_coefficients;
381 }
382}
383
384//////////////////////////////////////////////////////////////////////////
385template <typename PointT> void
387 const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields) const
388{
389 // Needs a valid set of model coefficients
390 if (!isModelValid (model_coefficients))
391 {
392 PCL_ERROR ("[pcl::SampleConsensusModelPlane::projectPoints] Given model is invalid!\n");
393 return;
394 }
395
396 projected_points.header = input_->header;
397 projected_points.is_dense = input_->is_dense;
398
399 Eigen::Vector4f mc (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
400
401 // normalize the vector perpendicular to the plane...
402 mc.normalize ();
403 // ... and store the resulting normal as a local copy of the model coefficients
404 Eigen::Vector4f tmp_mc = model_coefficients;
405 tmp_mc[0] = mc[0];
406 tmp_mc[1] = mc[1];
407 tmp_mc[2] = mc[2];
408
409 // Copy all the data fields from the input cloud to the projected one?
410 if (copy_data_fields)
411 {
412 // Allocate enough space and copy the basics
413 projected_points.resize (input_->size ());
414 projected_points.width = input_->width;
415 projected_points.height = input_->height;
416
417 using FieldList = typename pcl::traits::fieldList<PointT>::type;
418 // Iterate over each point
419 for (std::size_t i = 0; i < input_->size (); ++i)
420 // Iterate over each dimension
421 pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[i], projected_points[i]));
422
423 // Iterate through the 3d points and calculate the distances from them to the plane
424 for (const auto &inlier : inliers)
425 {
426 // Calculate the distance from the point to the plane
427 Eigen::Vector4f p ((*input_)[inlier].x,
428 (*input_)[inlier].y,
429 (*input_)[inlier].z,
430 1);
431 // use normalized coefficients to calculate the scalar projection
432 float distance_to_plane = tmp_mc.dot (p);
433
434 pcl::Vector4fMap pp = projected_points[inlier].getVector4fMap ();
435 pp.matrix () = p - mc * distance_to_plane; // mc[3] = 0, therefore the 3rd coordinate is safe
436 }
437 }
438 else
439 {
440 // Allocate enough space and copy the basics
441 projected_points.resize (inliers.size ());
442 projected_points.width = inliers.size ();
443 projected_points.height = 1;
444
445 using FieldList = typename pcl::traits::fieldList<PointT>::type;
446 // Iterate over each point
447 for (std::size_t i = 0; i < inliers.size (); ++i)
448 {
449 // Iterate over each dimension
450 pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[inliers[i]], projected_points[i]));
451 }
452
453 // Iterate through the 3d points and calculate the distances from them to the plane
454 for (std::size_t i = 0; i < inliers.size (); ++i)
455 {
456 // Calculate the distance from the point to the plane
457 Eigen::Vector4f p ((*input_)[inliers[i]].x,
458 (*input_)[inliers[i]].y,
459 (*input_)[inliers[i]].z,
460 1.0f);
461 // use normalized coefficients to calculate the scalar projection
462 float distance_to_plane = tmp_mc.dot (p);
463
464 pcl::Vector4fMap pp = projected_points[i].getVector4fMap ();
465 pp.matrix () = p - mc * distance_to_plane; // mc[3] = 0, therefore the 3rd coordinate is safe
466 }
467 }
468}
469
470//////////////////////////////////////////////////////////////////////////
471template <typename PointT> bool
473 const std::set<index_t> &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const
474{
475 // Needs a valid set of model coefficients
476 if (!isModelValid (model_coefficients))
477 {
478 PCL_ERROR ("[pcl::SampleConsensusModelPlane::doSamplesVerifyModel] Given model is invalid!\n");
479 return (false);
480 }
481
482 for (const auto &index : indices)
483 {
484 Eigen::Vector4f pt ((*input_)[index].x,
485 (*input_)[index].y,
486 (*input_)[index].z,
487 1.0f);
488 if (std::abs (model_coefficients.dot (pt)) > threshold)
489 {
490 return (false);
491 }
492 }
493
494 return (true);
495}
496
497#define PCL_INSTANTIATE_SampleConsensusModelPlane(T) template class PCL_EXPORTS pcl::SampleConsensusModelPlane<T>;
498
499#endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_PLANE_H_
500
Define methods for centroid estimation and covariance matrix calculus.
SampleConsensusModelPlane defines a model for 3D plane segmentation.
typename SampleConsensusModel< PointT >::PointCloud PointCloud
void optimizeModelCoefficients(const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const override
Recompute the plane coefficients using the given inlier set and return them to the user.
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const override
Compute all distances from the cloud data to a given plane model.
bool doSamplesVerifyModel(const std::set< index_t > &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const override
Verify whether a subset of indices verifies the given plane model coefficients.
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers) override
Select all the points which respect the given model coefficients as inliers.
void projectPoints(const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true) const override
Create a new point cloud with inliers projected onto the plane model.
bool computeModelCoefficients(const Indices &samples, Eigen::VectorXf &model_coefficients) const override
Check whether the given index samples can form a valid plane model, compute the model coefficients fr...
std::size_t countWithinDistanceStandard(const Eigen::VectorXf &model_coefficients, const double threshold, std::size_t i=0) const
This implementation uses no SIMD instructions.
std::size_t countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold) const override
Count all the points which respect the given model coefficients as inliers.
unsigned int computeMeanAndCovarianceMatrix(const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single lo...
Definition centroid.hpp:485
void eigen33(const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi d...
Definition eigen.hpp:296
Eigen::Map< Eigen::Vector4f, Eigen::Aligned > Vector4fMap
const Eigen::Map< const Eigen::Array4f, Eigen::Aligned > Array4fMapConst
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133