Point Cloud Library (PCL)  1.12.0-dev
ia_fpcs.hpp
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37 
38 #ifndef PCL_REGISTRATION_IMPL_IA_FPCS_H_
39 #define PCL_REGISTRATION_IMPL_IA_FPCS_H_
40 
41 #include <pcl/common/distances.h>
42 #include <pcl/common/time.h>
43 #include <pcl/common/utils.h>
44 #include <pcl/registration/ia_fpcs.h>
45 #include <pcl/registration/transformation_estimation_3point.h>
46 #include <pcl/sample_consensus/sac_model_plane.h>
47 
48 ///////////////////////////////////////////////////////////////////////////////////////////
49 template <typename PointT>
50 inline float
52  float max_dist,
53  int nr_threads)
54 {
55  const float max_dist_sqr = max_dist * max_dist;
56  const std::size_t s = cloud.size();
57 
59  tree.setInputCloud(cloud);
60 
61  float mean_dist = 0.f;
62  int num = 0;
63  pcl::Indices ids(2);
64  std::vector<float> dists_sqr(2);
65 
66  pcl::utils::ignore(nr_threads);
67 #pragma omp parallel for \
68  default(none) \
69  shared(tree, cloud) \
70  firstprivate(ids, dists_sqr) \
71  reduction(+:mean_dist, num) \
72  firstprivate(s, max_dist_sqr) \
73  num_threads(nr_threads)
74  for (int i = 0; i < 1000; i++) {
75  tree.nearestKSearch((*cloud)[rand() % s], 2, ids, dists_sqr);
76  if (dists_sqr[1] < max_dist_sqr) {
77  mean_dist += std::sqrt(dists_sqr[1]);
78  num++;
79  }
80  }
81 
82  return (mean_dist / num);
83 };
84 
85 ///////////////////////////////////////////////////////////////////////////////////////////
86 template <typename PointT>
87 inline float
89  const pcl::Indices& indices,
90  float max_dist,
91  int nr_threads)
92 {
93  const float max_dist_sqr = max_dist * max_dist;
94  const std::size_t s = indices.size();
95 
97  tree.setInputCloud(cloud);
98 
99  float mean_dist = 0.f;
100  int num = 0;
101  pcl::Indices ids(2);
102  std::vector<float> dists_sqr(2);
103 
104  pcl::utils::ignore(nr_threads);
105 #if OPENMP_LEGACY_CONST_DATA_SHARING_RULE
106 #pragma omp parallel for \
107  default(none) \
108  shared(tree, cloud, indices) \
109  firstprivate(ids, dists_sqr) \
110  reduction(+:mean_dist, num) \
111  num_threads(nr_threads)
112 #else
113 #pragma omp parallel for \
114  default(none) \
115  shared(tree, cloud, indices, s, max_dist_sqr) \
116  firstprivate(ids, dists_sqr) \
117  reduction(+:mean_dist, num) \
118  num_threads(nr_threads)
119 #endif
120  for (int i = 0; i < 1000; i++) {
121  tree.nearestKSearch((*cloud)[indices[rand() % s]], 2, ids, dists_sqr);
122  if (dists_sqr[1] < max_dist_sqr) {
123  mean_dist += std::sqrt(dists_sqr[1]);
124  num++;
125  }
126  }
127 
128  return (mean_dist / num);
129 };
130 
131 ///////////////////////////////////////////////////////////////////////////////////////////
132 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
135 : source_normals_()
136 , target_normals_()
137 , nr_threads_(1)
138 , approx_overlap_(0.5f)
139 , delta_(1.f)
140 , score_threshold_(FLT_MAX)
141 , nr_samples_(0)
142 , max_norm_diff_(90.f)
143 , max_runtime_(0)
144 , fitness_score_(FLT_MAX)
145 , diameter_()
146 , max_base_diameter_sqr_()
147 , use_normals_(false)
148 , normalize_delta_(true)
149 , max_pair_diff_()
150 , max_edge_diff_()
151 , coincidation_limit_()
152 , max_mse_()
153 , max_inlier_dist_sqr_()
154 , small_error_(0.00001f)
155 {
156  reg_name_ = "pcl::registration::FPCSInitialAlignment";
157  max_iterations_ = 0;
158  ransac_iterations_ = 1000;
159  transformation_estimation_.reset(
161 }
162 
163 ///////////////////////////////////////////////////////////////////////////////////////////
164 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
165 void
167  computeTransformation(PointCloudSource& output, const Eigen::Matrix4f& guess)
168 {
169  if (!initCompute())
170  return;
171 
172  final_transformation_ = guess;
173  bool abort = false;
174  std::vector<MatchingCandidates> all_candidates(max_iterations_);
175  pcl::StopWatch timer;
176 
177 #pragma omp parallel default(none) shared(abort, all_candidates, timer) \
178  num_threads(nr_threads_)
179  {
180 #ifdef _OPENMP
181  const unsigned int seed =
182  static_cast<unsigned int>(std::time(NULL)) ^ omp_get_thread_num();
183  std::srand(seed);
184  PCL_DEBUG("[%s::computeTransformation] Using seed=%u\n", reg_name_.c_str(), seed);
185 #pragma omp for schedule(dynamic)
186 #endif
187  for (int i = 0; i < max_iterations_; i++) {
188 #pragma omp flush(abort)
189 
190  MatchingCandidates candidates(1);
191  pcl::Indices base_indices(4);
192  all_candidates[i] = candidates;
193 
194  if (!abort) {
195  float ratio[2];
196  // select four coplanar point base
197  if (selectBase(base_indices, ratio) == 0) {
198  // calculate candidate pair correspondences using diagonal lengths of base
199  pcl::Correspondences pairs_a, pairs_b;
200  if (bruteForceCorrespondences(base_indices[0], base_indices[1], pairs_a) ==
201  0 &&
202  bruteForceCorrespondences(base_indices[2], base_indices[3], pairs_b) ==
203  0) {
204  // determine candidate matches by combining pair correspondences based on
205  // segment distances
206  std::vector<pcl::Indices> matches;
207  if (determineBaseMatches(base_indices, matches, pairs_a, pairs_b, ratio) ==
208  0) {
209  // check and evaluate candidate matches and store them
210  handleMatches(base_indices, matches, candidates);
211  if (!candidates.empty())
212  all_candidates[i] = candidates;
213  }
214  }
215  }
216 
217  // check terminate early (time or fitness_score threshold reached)
218  abort = (!candidates.empty() ? candidates[0].fitness_score < score_threshold_
219  : abort);
220  abort = (abort ? abort : timer.getTimeSeconds() > max_runtime_);
221 
222 #pragma omp flush(abort)
223  }
224  }
225  }
226 
227  // determine best match over all tries
228  finalCompute(all_candidates);
229 
230  // apply the final transformation
231  pcl::transformPointCloud(*input_, output, final_transformation_);
232 
233  deinitCompute();
234 }
235 
236 ///////////////////////////////////////////////////////////////////////////////////////////
237 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
238 bool
241 {
242  const unsigned int seed = std::time(nullptr);
243  std::srand(seed);
244  PCL_DEBUG("[%s::initCompute] Using seed=%u\n", reg_name_.c_str(), seed);
245 
246  // basic pcl initialization
248  return (false);
249 
250  // check if source and target are given
251  if (!input_ || !target_) {
252  PCL_ERROR("[%s::initCompute] Source or target dataset not given!\n",
253  reg_name_.c_str());
254  return (false);
255  }
256 
257  if (!target_indices_ || target_indices_->empty()) {
258  target_indices_.reset(new pcl::Indices(target_->size()));
259  int index = 0;
260  for (auto& target_index : *target_indices_)
261  target_index = index++;
262  target_cloud_updated_ = true;
263  }
264 
265  // if a sample size for the point clouds is given; prefarably no sampling of target
266  // cloud
267  if (nr_samples_ != 0) {
268  const int ss = static_cast<int>(indices_->size());
269  const int sample_fraction_src = std::max(1, static_cast<int>(ss / nr_samples_));
270 
271  source_indices_ = pcl::IndicesPtr(new pcl::Indices);
272  for (int i = 0; i < ss; i++)
273  if (rand() % sample_fraction_src == 0)
274  source_indices_->push_back((*indices_)[i]);
275  }
276  else
277  source_indices_ = indices_;
278 
279  // check usage of normals
280  if (source_normals_ && target_normals_ && source_normals_->size() == input_->size() &&
281  target_normals_->size() == target_->size())
282  use_normals_ = true;
283 
284  // set up tree structures
285  if (target_cloud_updated_) {
286  tree_->setInputCloud(target_, target_indices_);
287  target_cloud_updated_ = false;
288  }
289 
290  // set predefined variables
291  const int min_iterations = 4;
292  const float diameter_fraction = 0.3f;
293 
294  // get diameter of input cloud (distance between farthest points)
295  Eigen::Vector4f pt_min, pt_max;
296  pcl::getMinMax3D(*target_, *target_indices_, pt_min, pt_max);
297  diameter_ = (pt_max - pt_min).norm();
298 
299  // derive the limits for the random base selection
300  float max_base_diameter = diameter_ * approx_overlap_ * 2.f;
301  max_base_diameter_sqr_ = max_base_diameter * max_base_diameter;
302 
303  // normalize the delta
304  if (normalize_delta_) {
305  float mean_dist = getMeanPointDensity<PointTarget>(
306  target_, *target_indices_, 0.05f * diameter_, nr_threads_);
307  delta_ *= mean_dist;
308  }
309 
310  // heuristic determination of number of trials to have high probability of finding a
311  // good solution
312  if (max_iterations_ == 0) {
313  float first_est =
314  std::log(small_error_) /
315  std::log(1.0 - std::pow((double)approx_overlap_, (double)min_iterations));
316  max_iterations_ =
317  static_cast<int>(first_est / (diameter_fraction * approx_overlap_ * 2.f));
318  }
319 
320  // set further parameter
321  if (score_threshold_ == FLT_MAX)
322  score_threshold_ = 1.f - approx_overlap_;
323 
324  if (max_iterations_ < 4)
325  max_iterations_ = 4;
326 
327  if (max_runtime_ < 1)
328  max_runtime_ = INT_MAX;
329 
330  // calculate internal parameters based on the the estimated point density
331  max_pair_diff_ = delta_ * 2.f;
332  max_edge_diff_ = delta_ * 4.f;
333  coincidation_limit_ = delta_ * 2.f; // EDITED: originally std::sqrt (delta_ * 2.f)
334  max_mse_ = powf(delta_ * 2.f, 2.f);
335  max_inlier_dist_sqr_ = powf(delta_ * 2.f, 2.f);
336 
337  // reset fitness_score
338  fitness_score_ = FLT_MAX;
339 
340  return (true);
341 }
342 
343 ///////////////////////////////////////////////////////////////////////////////////////////
344 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
345 int
347  selectBase(pcl::Indices& base_indices, float (&ratio)[2])
348 {
349  const float too_close_sqr = max_base_diameter_sqr_ * 0.01;
350 
351  Eigen::VectorXf coefficients(4);
353  plane.setIndices(target_indices_);
354  Eigen::Vector4f centre_pt;
355  float nearest_to_plane = FLT_MAX;
356 
357  // repeat base search until valid quadruple was found or ransac_iterations_ number of
358  // tries were unsuccessful
359  for (int i = 0; i < ransac_iterations_; i++) {
360  // random select an appropriate point triple
361  if (selectBaseTriangle(base_indices) < 0)
362  continue;
363 
364  pcl::Indices base_triple(base_indices.begin(), base_indices.end() - 1);
365  plane.computeModelCoefficients(base_triple, coefficients);
366  pcl::compute3DCentroid(*target_, base_triple, centre_pt);
367 
368  // loop over all points in source cloud to find most suitable fourth point
369  const PointTarget* pt1 = &((*target_)[base_indices[0]]);
370  const PointTarget* pt2 = &((*target_)[base_indices[1]]);
371  const PointTarget* pt3 = &((*target_)[base_indices[2]]);
372 
373  for (const auto& target_index : *target_indices_) {
374  const PointTarget* pt4 = &((*target_)[target_index]);
375 
376  float d1 = pcl::squaredEuclideanDistance(*pt4, *pt1);
377  float d2 = pcl::squaredEuclideanDistance(*pt4, *pt2);
378  float d3 = pcl::squaredEuclideanDistance(*pt4, *pt3);
379  float d4 = (pt4->getVector3fMap() - centre_pt.head(3)).squaredNorm();
380 
381  // check distance between points w.r.t minimum sampling distance; EDITED -> 4th
382  // point now also limited by max base line
383  if (d1 < too_close_sqr || d2 < too_close_sqr || d3 < too_close_sqr ||
384  d4 < too_close_sqr || d1 > max_base_diameter_sqr_ ||
385  d2 > max_base_diameter_sqr_ || d3 > max_base_diameter_sqr_)
386  continue;
387 
388  // check distance to plane to get point closest to plane
389  float dist_to_plane = pcl::pointToPlaneDistance(*pt4, coefficients);
390  if (dist_to_plane < nearest_to_plane) {
391  base_indices[3] = target_index;
392  nearest_to_plane = dist_to_plane;
393  }
394  }
395 
396  // check if at least one point fulfilled the conditions
397  if (nearest_to_plane != FLT_MAX) {
398  // order points to build largest quadrangle and calcuate intersection ratios of
399  // diagonals
400  setupBase(base_indices, ratio);
401  return (0);
402  }
403  }
404 
405  // return unsuccessful if no quadruple was selected
406  return (-1);
407 }
408 
409 ///////////////////////////////////////////////////////////////////////////////////////////
410 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
411 int
414 {
415  const auto nr_points = target_indices_->size();
416  float best_t = 0.f;
417 
418  // choose random first point
419  base_indices[0] = (*target_indices_)[rand() % nr_points];
420  auto* index1 = &base_indices[0];
421 
422  // random search for 2 other points (as far away as overlap allows)
423  for (int i = 0; i < ransac_iterations_; i++) {
424  auto* index2 = &(*target_indices_)[rand() % nr_points];
425  auto* index3 = &(*target_indices_)[rand() % nr_points];
426 
427  Eigen::Vector3f u =
428  (*target_)[*index2].getVector3fMap() - (*target_)[*index1].getVector3fMap();
429  Eigen::Vector3f v =
430  (*target_)[*index3].getVector3fMap() - (*target_)[*index1].getVector3fMap();
431  float t =
432  u.cross(v).squaredNorm(); // triangle area (0.5 * sqrt(t)) should be maximal
433 
434  // check for most suitable point triple
435  if (t > best_t && u.squaredNorm() < max_base_diameter_sqr_ &&
436  v.squaredNorm() < max_base_diameter_sqr_) {
437  best_t = t;
438  base_indices[1] = *index2;
439  base_indices[2] = *index3;
440  }
441  }
442 
443  // return if a triplet could be selected
444  return (best_t == 0.f ? -1 : 0);
445 }
446 
447 ///////////////////////////////////////////////////////////////////////////////////////////
448 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
449 void
451  setupBase(pcl::Indices& base_indices, float (&ratio)[2])
452 {
453  float best_t = FLT_MAX;
454  const pcl::Indices copy(base_indices.begin(), base_indices.end());
455  pcl::Indices temp(base_indices.begin(), base_indices.end());
456 
457  // loop over all combinations of base points
458  for (auto i = copy.begin(), i_e = copy.end(); i != i_e; ++i)
459  for (auto j = copy.begin(), j_e = copy.end(); j != j_e; ++j) {
460  if (i == j)
461  continue;
462 
463  for (auto k = copy.begin(), k_e = copy.end(); k != k_e; ++k) {
464  if (k == j || k == i)
465  continue;
466 
467  auto l = copy.begin();
468  while (l == i || l == j || l == k)
469  ++l;
470 
471  temp[0] = *i;
472  temp[1] = *j;
473  temp[2] = *k;
474  temp[3] = *l;
475 
476  // calculate diagonal intersection ratios and check for suitable segment to
477  // segment distances
478  float ratio_temp[2];
479  float t = segmentToSegmentDist(temp, ratio_temp);
480  if (t < best_t) {
481  best_t = t;
482  ratio[0] = ratio_temp[0];
483  ratio[1] = ratio_temp[1];
484  base_indices = temp;
485  }
486  }
487  }
488 }
489 
490 ///////////////////////////////////////////////////////////////////////////////////////////
491 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
492 float
494  segmentToSegmentDist(const pcl::Indices& base_indices, float (&ratio)[2])
495 {
496  // get point vectors
497  Eigen::Vector3f u = (*target_)[base_indices[1]].getVector3fMap() -
498  (*target_)[base_indices[0]].getVector3fMap();
499  Eigen::Vector3f v = (*target_)[base_indices[3]].getVector3fMap() -
500  (*target_)[base_indices[2]].getVector3fMap();
501  Eigen::Vector3f w = (*target_)[base_indices[0]].getVector3fMap() -
502  (*target_)[base_indices[2]].getVector3fMap();
503 
504  // calculate segment distances
505  float a = u.dot(u);
506  float b = u.dot(v);
507  float c = v.dot(v);
508  float d = u.dot(w);
509  float e = v.dot(w);
510  float D = a * c - b * b;
511  float sN = 0.f, sD = D;
512  float tN = 0.f, tD = D;
513 
514  // check segments
515  if (D < small_error_) {
516  sN = 0.f;
517  sD = 1.f;
518  tN = e;
519  tD = c;
520  }
521  else {
522  sN = (b * e - c * d);
523  tN = (a * e - b * d);
524 
525  if (sN < 0.f) {
526  sN = 0.f;
527  tN = e;
528  tD = c;
529  }
530  else if (sN > sD) {
531  sN = sD;
532  tN = e + b;
533  tD = c;
534  }
535  }
536 
537  if (tN < 0.f) {
538  tN = 0.f;
539 
540  if (-d < 0.f)
541  sN = 0.f;
542 
543  else if (-d > a)
544  sN = sD;
545 
546  else {
547  sN = -d;
548  sD = a;
549  }
550  }
551 
552  else if (tN > tD) {
553  tN = tD;
554 
555  if ((-d + b) < 0.f)
556  sN = 0.f;
557 
558  else if ((-d + b) > a)
559  sN = sD;
560 
561  else {
562  sN = (-d + b);
563  sD = a;
564  }
565  }
566 
567  // set intersection ratios
568  ratio[0] = (std::abs(sN) < small_error_) ? 0.f : sN / sD;
569  ratio[1] = (std::abs(tN) < small_error_) ? 0.f : tN / tD;
570 
571  Eigen::Vector3f x = w + (ratio[0] * u) - (ratio[1] * v);
572  return (x.norm());
573 }
574 
575 ///////////////////////////////////////////////////////////////////////////////////////////
576 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
577 int
580 {
581  const float max_norm_diff = 0.5f * max_norm_diff_ * M_PI / 180.f;
582 
583  // calculate reference segment distance and normal angle
584  float ref_dist = pcl::euclideanDistance((*target_)[idx1], (*target_)[idx2]);
585  float ref_norm_angle =
586  (use_normals_ ? ((*target_normals_)[idx1].getNormalVector3fMap() -
587  (*target_normals_)[idx2].getNormalVector3fMap())
588  .norm()
589  : 0.f);
590 
591  // loop over all pairs of points in source point cloud
592  auto it_out = source_indices_->begin(), it_out_e = source_indices_->end() - 1;
593  auto it_in_e = source_indices_->end();
594  for (; it_out != it_out_e; it_out++) {
595  auto it_in = it_out + 1;
596  const PointSource* pt1 = &(*input_)[*it_out];
597  for (; it_in != it_in_e; it_in++) {
598  const PointSource* pt2 = &(*input_)[*it_in];
599 
600  // check point distance compared to reference dist (from base)
601  float dist = pcl::euclideanDistance(*pt1, *pt2);
602  if (std::abs(dist - ref_dist) < max_pair_diff_) {
603  // add here normal evaluation if normals are given
604  if (use_normals_) {
605  const NormalT* pt1_n = &((*source_normals_)[*it_out]);
606  const NormalT* pt2_n = &((*source_normals_)[*it_in]);
607 
608  float norm_angle_1 =
609  (pt1_n->getNormalVector3fMap() - pt2_n->getNormalVector3fMap()).norm();
610  float norm_angle_2 =
611  (pt1_n->getNormalVector3fMap() + pt2_n->getNormalVector3fMap()).norm();
612 
613  float norm_diff = std::min<float>(std::abs(norm_angle_1 - ref_norm_angle),
614  std::abs(norm_angle_2 - ref_norm_angle));
615  if (norm_diff > max_norm_diff)
616  continue;
617  }
618 
619  pairs.push_back(pcl::Correspondence(*it_in, *it_out, dist));
620  pairs.push_back(pcl::Correspondence(*it_out, *it_in, dist));
621  }
622  }
623  }
624 
625  // return success if at least one correspondence was found
626  return (pairs.empty() ? -1 : 0);
627 }
628 
629 ///////////////////////////////////////////////////////////////////////////////////////////
630 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
631 int
634  std::vector<pcl::Indices>& matches,
635  const pcl::Correspondences& pairs_a,
636  const pcl::Correspondences& pairs_b,
637  const float (&ratio)[2])
638 {
639  // calculate edge lengths of base
640  float dist_base[4];
641  dist_base[0] =
642  pcl::euclideanDistance((*target_)[base_indices[0]], (*target_)[base_indices[2]]);
643  dist_base[1] =
644  pcl::euclideanDistance((*target_)[base_indices[0]], (*target_)[base_indices[3]]);
645  dist_base[2] =
646  pcl::euclideanDistance((*target_)[base_indices[1]], (*target_)[base_indices[2]]);
647  dist_base[3] =
648  pcl::euclideanDistance((*target_)[base_indices[1]], (*target_)[base_indices[3]]);
649 
650  // loop over first point pair correspondences and store intermediate points 'e' in new
651  // point cloud
653  cloud_e->resize(pairs_a.size() * 2);
654  PointCloudSourceIterator it_pt = cloud_e->begin();
655  for (const auto& pair : pairs_a) {
656  const PointSource* pt1 = &((*input_)[pair.index_match]);
657  const PointSource* pt2 = &((*input_)[pair.index_query]);
658 
659  // calculate intermediate points using both ratios from base (r1,r2)
660  for (int i = 0; i < 2; i++, it_pt++) {
661  it_pt->x = pt1->x + ratio[i] * (pt2->x - pt1->x);
662  it_pt->y = pt1->y + ratio[i] * (pt2->y - pt1->y);
663  it_pt->z = pt1->z + ratio[i] * (pt2->z - pt1->z);
664  }
665  }
666 
667  // initialize new kd tree of intermediate points from first point pair correspondences
669  tree_e->setInputCloud(cloud_e);
670 
671  pcl::Indices ids;
672  std::vector<float> dists_sqr;
673 
674  // loop over second point pair correspondences
675  for (const auto& pair : pairs_b) {
676  const PointTarget* pt1 = &((*input_)[pair.index_match]);
677  const PointTarget* pt2 = &((*input_)[pair.index_query]);
678 
679  // calculate intermediate points using both ratios from base (r1,r2)
680  for (const float& r : ratio) {
681  PointTarget pt_e;
682  pt_e.x = pt1->x + r * (pt2->x - pt1->x);
683  pt_e.y = pt1->y + r * (pt2->y - pt1->y);
684  pt_e.z = pt1->z + r * (pt2->z - pt1->z);
685 
686  // search for corresponding intermediate points
687  tree_e->radiusSearch(pt_e, coincidation_limit_, ids, dists_sqr);
688  for (const auto& id : ids) {
689  pcl::Indices match_indices(4);
690 
691  match_indices[0] =
692  pairs_a[static_cast<int>(std::floor((float)(id / 2.f)))].index_match;
693  match_indices[1] =
694  pairs_a[static_cast<int>(std::floor((float)(id / 2.f)))].index_query;
695  match_indices[2] = pair.index_match;
696  match_indices[3] = pair.index_query;
697 
698  // EDITED: added coarse check of match based on edge length (due to rigid-body )
699  if (checkBaseMatch(match_indices, dist_base) < 0)
700  continue;
701 
702  matches.push_back(match_indices);
703  }
704  }
705  }
706 
707  // return unsuccessful if no match was found
708  return (!matches.empty() ? 0 : -1);
709 }
710 
711 ///////////////////////////////////////////////////////////////////////////////////////////
712 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
713 int
715  checkBaseMatch(const pcl::Indices& match_indices, const float (&dist_ref)[4])
716 {
717  float d0 =
718  pcl::euclideanDistance((*input_)[match_indices[0]], (*input_)[match_indices[2]]);
719  float d1 =
720  pcl::euclideanDistance((*input_)[match_indices[0]], (*input_)[match_indices[3]]);
721  float d2 =
722  pcl::euclideanDistance((*input_)[match_indices[1]], (*input_)[match_indices[2]]);
723  float d3 =
724  pcl::euclideanDistance((*input_)[match_indices[1]], (*input_)[match_indices[3]]);
725 
726  // check edge distances of match w.r.t the base
727  return (std::abs(d0 - dist_ref[0]) < max_edge_diff_ &&
728  std::abs(d1 - dist_ref[1]) < max_edge_diff_ &&
729  std::abs(d2 - dist_ref[2]) < max_edge_diff_ &&
730  std::abs(d3 - dist_ref[3]) < max_edge_diff_)
731  ? 0
732  : -1;
733 }
734 
735 ///////////////////////////////////////////////////////////////////////////////////////////
736 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
737 void
739  handleMatches(const pcl::Indices& base_indices,
740  std::vector<pcl::Indices>& matches,
741  MatchingCandidates& candidates)
742 {
743  candidates.resize(1);
744  float fitness_score = FLT_MAX;
745 
746  // loop over all Candidate matches
747  for (auto& match : matches) {
748  Eigen::Matrix4f transformation_temp;
749  pcl::Correspondences correspondences_temp;
750 
751  // determine corresondences between base and match according to their distance to
752  // centroid
753  linkMatchWithBase(base_indices, match, correspondences_temp);
754 
755  // check match based on residuals of the corresponding points after
756  if (validateMatch(base_indices, match, correspondences_temp, transformation_temp) <
757  0)
758  continue;
759 
760  // check resulting using a sub sample of the source point cloud and compare to
761  // previous matches
762  if (validateTransformation(transformation_temp, fitness_score) < 0)
763  continue;
764 
765  // store best match as well as associated fitness_score and transformation
766  candidates[0].fitness_score = fitness_score;
767  candidates[0].transformation = transformation_temp;
768  correspondences_temp.erase(correspondences_temp.end() - 1);
769  candidates[0].correspondences = correspondences_temp;
770  }
771 }
772 
773 ///////////////////////////////////////////////////////////////////////////////////////////
774 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
775 void
777  linkMatchWithBase(const pcl::Indices& base_indices,
778  pcl::Indices& match_indices,
779  pcl::Correspondences& correspondences)
780 {
781  // calculate centroid of base and target
782  Eigen::Vector4f centre_base{0, 0, 0, 0}, centre_match{0, 0, 0, 0};
783  pcl::compute3DCentroid(*target_, base_indices, centre_base);
784  pcl::compute3DCentroid(*input_, match_indices, centre_match);
785 
786  PointTarget centre_pt_base;
787  centre_pt_base.x = centre_base[0];
788  centre_pt_base.y = centre_base[1];
789  centre_pt_base.z = centre_base[2];
790 
791  PointSource centre_pt_match;
792  centre_pt_match.x = centre_match[0];
793  centre_pt_match.y = centre_match[1];
794  centre_pt_match.z = centre_match[2];
795 
796  // find corresponding points according to their distance to the centroid
797  pcl::Indices copy = match_indices;
798 
799  auto it_match_orig = match_indices.begin();
800  for (auto it_base = base_indices.cbegin(), it_base_e = base_indices.cend();
801  it_base != it_base_e;
802  it_base++, it_match_orig++) {
803  float dist_sqr_1 =
804  pcl::squaredEuclideanDistance((*target_)[*it_base], centre_pt_base);
805  float best_diff_sqr = FLT_MAX;
806  int best_index = -1;
807 
808  for (const auto& match_index : copy) {
809  // calculate difference of distances to centre point
810  float dist_sqr_2 =
811  pcl::squaredEuclideanDistance((*input_)[match_index], centre_pt_match);
812  float diff_sqr = std::abs(dist_sqr_1 - dist_sqr_2);
813 
814  if (diff_sqr < best_diff_sqr) {
815  best_diff_sqr = diff_sqr;
816  best_index = match_index;
817  }
818  }
819 
820  // assign new correspondence and update indices of matched targets
821  correspondences.push_back(pcl::Correspondence(best_index, *it_base, best_diff_sqr));
822  *it_match_orig = best_index;
823  }
824 }
825 
826 ///////////////////////////////////////////////////////////////////////////////////////////
827 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
828 int
830  validateMatch(const pcl::Indices& base_indices,
831  const pcl::Indices& match_indices,
832  const pcl::Correspondences& correspondences,
833  Eigen::Matrix4f& transformation)
834 {
835  // only use triplet of points to simlify process (possible due to planar case)
836  pcl::Correspondences correspondences_temp = correspondences;
837  correspondences_temp.erase(correspondences_temp.end() - 1);
838 
839  // estimate transformation between correspondence set
840  transformation_estimation_->estimateRigidTransformation(
841  *input_, *target_, correspondences_temp, transformation);
842 
843  // transform base points
844  PointCloudSource match_transformed;
845  pcl::transformPointCloud(*input_, match_indices, match_transformed, transformation);
846 
847  // calculate residuals of transformation and check against maximum threshold
848  std::size_t nr_points = correspondences_temp.size();
849  float mse = 0.f;
850  for (std::size_t i = 0; i < nr_points; i++)
851  mse += pcl::squaredEuclideanDistance(match_transformed.points[i],
852  target_->points[base_indices[i]]);
853 
854  mse /= nr_points;
855  return (mse < max_mse_ ? 0 : -1);
856 }
857 
858 ///////////////////////////////////////////////////////////////////////////////////////////
859 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
860 int
862  validateTransformation(Eigen::Matrix4f& transformation, float& fitness_score)
863 {
864  // transform source point cloud
865  PointCloudSource source_transformed;
867  *input_, *source_indices_, source_transformed, transformation);
868 
869  std::size_t nr_points = source_transformed.size();
870  std::size_t terminate_value =
871  fitness_score > 1 ? 0
872  : static_cast<std::size_t>((1.f - fitness_score) * nr_points);
873 
874  float inlier_score_temp = 0;
875  pcl::Indices ids;
876  std::vector<float> dists_sqr;
877  PointCloudSourceIterator it = source_transformed.begin();
878 
879  for (std::size_t i = 0; i < nr_points; it++, i++) {
880  // search for nearest point using kd tree search
881  tree_->nearestKSearch(*it, 1, ids, dists_sqr);
882  inlier_score_temp += (dists_sqr[0] < max_inlier_dist_sqr_ ? 1 : 0);
883 
884  // early terminating
885  if (nr_points - i + inlier_score_temp < terminate_value)
886  break;
887  }
888 
889  // check current costs and return unsuccessful if larger than previous ones
890  inlier_score_temp /= static_cast<float>(nr_points);
891  float fitness_score_temp = 1.f - inlier_score_temp;
892 
893  if (fitness_score_temp > fitness_score)
894  return (-1);
895 
896  fitness_score = fitness_score_temp;
897  return (0);
898 }
899 
900 ///////////////////////////////////////////////////////////////////////////////////////////
901 template <typename PointSource, typename PointTarget, typename NormalT, typename Scalar>
902 void
904  finalCompute(const std::vector<MatchingCandidates>& candidates)
905 {
906  // get best fitness_score over all tries
907  int nr_candidates = static_cast<int>(candidates.size());
908  int best_index = -1;
909  float best_score = FLT_MAX;
910  for (int i = 0; i < nr_candidates; i++) {
911  const float& fitness_score = candidates[i][0].fitness_score;
912  if (fitness_score < best_score) {
913  best_score = fitness_score;
914  best_index = i;
915  }
916  }
917 
918  // check if a valid candidate was available
919  if (!(best_index < 0)) {
920  fitness_score_ = candidates[best_index][0].fitness_score;
921  final_transformation_ = candidates[best_index][0].transformation;
922  *correspondences_ = candidates[best_index][0].correspondences;
923 
924  // here we define convergence if resulting fitness_score is below 1-threshold
925  converged_ = fitness_score_ < score_threshold_;
926  }
927 }
928 
929 ///////////////////////////////////////////////////////////////////////////////////////////
930 
931 #endif // PCL_REGISTRATION_IMPL_IA_4PCS_H_
pcl::Normal
A point structure representing normal coordinates and the surface curvature estimate.
Definition: point_types.hpp:899
pcl::StopWatch
Simple stopwatch.
Definition: time.h:58
pcl::IndicesPtr
shared_ptr< Indices > IndicesPtr
Definition: pcl_base.h:58
pcl::search::KdTree::nearestKSearch
int nearestKSearch(const PointT &point, int k, Indices &k_indices, std::vector< float > &k_sqr_distances) const override
Search for the k-nearest neighbors for the given query point.
Definition: kdtree.hpp:87
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:395
pcl::SampleConsensusModelPlane
SampleConsensusModelPlane defines a model for 3D plane segmentation.
Definition: sac_model_plane.h:142
pcl::registration::FPCSInitialAlignment::FPCSInitialAlignment
FPCSInitialAlignment()
Constructor.
Definition: ia_fpcs.hpp:134
pcl::PointCloud::begin
iterator begin() noexcept
Definition: point_cloud.h:429
pcl::search::KdTree::setInputCloud
void setInputCloud(const PointCloudConstPtr &cloud, const IndicesConstPtr &indices=IndicesConstPtr()) override
Provide a pointer to the input dataset.
Definition: kdtree.hpp:76
pcl::registration::FPCSInitialAlignment::computeTransformation
void computeTransformation(PointCloudSource &output, const Eigen::Matrix4f &guess) override
Rigid transformation computation method.
Definition: ia_fpcs.hpp:167
pcl::registration::FPCSInitialAlignment::validateTransformation
virtual int validateTransformation(Eigen::Matrix4f &transformation, float &fitness_score)
Validate the transformation by calculating the number of inliers after transforming the source cloud.
Definition: ia_fpcs.hpp:862
pcl::euclideanDistance
float euclideanDistance(const PointType1 &p1, const PointType2 &p2)
Calculate the euclidean distance between the two given points.
Definition: distances.h:204
pcl::PCLBase
PCL base class.
Definition: pcl_base.h:69
pcl::registration::FPCSInitialAlignment::handleMatches
virtual void handleMatches(const pcl::Indices &base_indices, std::vector< pcl::Indices > &matches, MatchingCandidates &candidates)
Method to handle current candidate matches.
Definition: ia_fpcs.hpp:739
pcl::PointCloud< PointSource >
pcl::registration::FPCSInitialAlignment::segmentToSegmentDist
float segmentToSegmentDist(const pcl::Indices &base_indices, float(&ratio)[2])
Calculate intersection ratios and segment to segment distances of base diagonals.
Definition: ia_fpcs.hpp:494
pcl::pointToPlaneDistance
double pointToPlaneDistance(const Point &p, double a, double b, double c, double d)
Get the distance from a point to a plane (unsigned) defined by ax+by+cz+d=0.
Definition: sac_model_plane.h:114
pcl::transformPointCloud
void transformPointCloud(const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform, bool copy_all_fields)
Apply a rigid transform defined by a 4x4 matrix.
Definition: transforms.hpp:221
pcl::Registration< PointSource, PointTarget, float >::PointCloudSourcePtr
typename PointCloudSource::Ptr PointCloudSourcePtr
Definition: registration.h:77
pcl::registration::MatchingCandidates
std::vector< MatchingCandidate, Eigen::aligned_allocator< MatchingCandidate > > MatchingCandidates
Definition: matching_candidate.h:79
pcl::squaredEuclideanDistance
float squaredEuclideanDistance(const PointType1 &p1, const PointType2 &p2)
Calculate the squared euclidean distance between the two given points.
Definition: distances.h:182
pcl::SampleConsensusModelPlane::computeModelCoefficients
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...
Definition: sac_model_plane.hpp:70
pcl::registration::FPCSInitialAlignment::selectBase
int selectBase(pcl::Indices &base_indices, float(&ratio)[2])
Select an approximately coplanar set of four points from the source cloud.
Definition: ia_fpcs.hpp:347
pcl::SampleConsensusModel::setIndices
void setIndices(const IndicesPtr &indices)
Provide a pointer to the vector of indices that represents the input data.
Definition: sac_model.h:323
M_PI
#define M_PI
Definition: pcl_macros.h:201
pcl::getMeanPointDensity
float getMeanPointDensity(const typename pcl::PointCloud< PointT >::ConstPtr &cloud, float max_dist, int nr_threads=1)
Compute the mean point density of a given point cloud.
Definition: ia_fpcs.hpp:51
pcl::search::KdTree< PointT >
pcl::registration::FPCSInitialAlignment::selectBaseTriangle
int selectBaseTriangle(pcl::Indices &base_indices)
Select randomly a triplet of points with large point-to-point distances.
Definition: ia_fpcs.hpp:413
pcl::registration::FPCSInitialAlignment::finalCompute
virtual void finalCompute(const std::vector< MatchingCandidates > &candidates)
Final computation of best match out of vector of best matches.
Definition: ia_fpcs.hpp:904
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
pcl::registration::FPCSInitialAlignment::setupBase
void setupBase(pcl::Indices &base_indices, float(&ratio)[2])
Setup the base (four coplanar points) by ordering the points and computing intersection ratios and se...
Definition: ia_fpcs.hpp:451
pcl::registration::FPCSInitialAlignment::linkMatchWithBase
virtual void linkMatchWithBase(const pcl::Indices &base_indices, pcl::Indices &match_indices, pcl::Correspondences &correspondences)
Sets the correspondences between the base B and the match M by using the distance of each point to th...
Definition: ia_fpcs.hpp:777
pcl::PointCloud::size
std::size_t size() const
Definition: point_cloud.h:443
pcl::Registration< PointSource, PointTarget, float >::KdTreeReciprocalPtr
typename KdTreeReciprocal::Ptr KdTreeReciprocalPtr
Definition: registration.h:74
pcl::getMinMax3D
void getMinMax3D(const pcl::PointCloud< PointT > &cloud, PointT &min_pt, PointT &max_pt)
Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.
Definition: common.hpp:295
pcl::registration::FPCSInitialAlignment::validateMatch
virtual int validateMatch(const pcl::Indices &base_indices, const pcl::Indices &match_indices, const pcl::Correspondences &correspondences, Eigen::Matrix4f &transformation)
Validate the matching by computing the transformation between the source and target based on the four...
Definition: ia_fpcs.hpp:830
pcl::utils::ignore
void ignore(const T &...)
Utility function to eliminate unused variable warnings.
Definition: utils.h:62
time.h
pcl::PointCloud::ConstPtr
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:414
pcl::registration::FPCSInitialAlignment::checkBaseMatch
int checkBaseMatch(const pcl::Indices &match_indices, const float(&ds)[4])
Check if outer rectangle distance of matched points fit with the base rectangle.
Definition: ia_fpcs.hpp:715
pcl::compute3DCentroid
unsigned int compute3DCentroid(ConstCloudIterator< PointT > &cloud_iterator, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
Definition: centroid.hpp:56
distances.h
pcl::registration::FPCSInitialAlignment::determineBaseMatches
virtual int determineBaseMatches(const pcl::Indices &base_indices, std::vector< pcl::Indices > &matches, const pcl::Correspondences &pairs_a, const pcl::Correspondences &pairs_b, const float(&ratio)[2])
Determine base matches by combining the point pair candidate and search for coinciding intersection p...
Definition: ia_fpcs.hpp:633
pcl::Correspondences
std::vector< pcl::Correspondence, Eigen::aligned_allocator< pcl::Correspondence > > Correspondences
Definition: correspondence.h:89
pcl::StopWatch::getTimeSeconds
double getTimeSeconds() const
Retrieve the time in seconds spent since the last call to reset().
Definition: time.h:76
pcl::registration::TransformationEstimation3Point
TransformationEstimation3Points represents the class for transformation estimation based on:
Definition: transformation_estimation_3point.h:58
pcl::Correspondence
Correspondence represents a match between two entities (e.g., points, descriptors,...
Definition: correspondence.h:60
pcl::registration::FPCSInitialAlignment::bruteForceCorrespondences
virtual int bruteForceCorrespondences(int idx1, int idx2, pcl::Correspondences &pairs)
Search for corresponding point pairs given the distance between two base points.
Definition: ia_fpcs.hpp:579
pcl::registration::FPCSInitialAlignment::initCompute
virtual bool initCompute()
Internal computation initialization.
Definition: ia_fpcs.hpp:240