Point Cloud Library (PCL)  1.9.1-dev
rmsac.hpp
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40 
41 #ifndef PCL_SAMPLE_CONSENSUS_IMPL_RMSAC_H_
42 #define PCL_SAMPLE_CONSENSUS_IMPL_RMSAC_H_
43 
44 #include <pcl/sample_consensus/rmsac.h>
45 
46 //////////////////////////////////////////////////////////////////////////
47 template <typename PointT> bool
49 {
50  // Warn and exit if no threshold was set
51  if (threshold_ == std::numeric_limits<double>::max())
52  {
53  PCL_ERROR ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] No threshold set!\n");
54  return (false);
55  }
56 
57  iterations_ = 0;
58  double d_best_penalty = std::numeric_limits<double>::max();
59  double k = 1.0;
60 
61  std::vector<int> best_model;
62  std::vector<int> selection;
63  Eigen::VectorXf model_coefficients;
64  std::vector<double> distances;
65  std::set<int> indices_subset;
66 
67  int n_inliers_count = 0;
68  unsigned skipped_count = 0;
69  // suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
70  const unsigned max_skip = max_iterations_ * 10;
71 
72  // Number of samples to try randomly
73  std::size_t fraction_nr_points = pcl_lrint (static_cast<double>(sac_model_->getIndices ()->size ()) * fraction_nr_pretest_ / 100.0);
74 
75  // Iterate
76  while (iterations_ < k && skipped_count < max_skip)
77  {
78  // Get X samples which satisfy the model criteria
79  sac_model_->getSamples (iterations_, selection);
80 
81  if (selection.empty ()) break;
82 
83  // Search for inliers in the point cloud for the current plane model M
84  if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
85  {
86  //iterations_++;
87  ++ skipped_count;
88  continue;
89  }
90 
91  // RMSAC addon: verify a random fraction of the data
92  // Get X random samples which satisfy the model criterion
93  this->getRandomSamples (sac_model_->getIndices (), fraction_nr_points, indices_subset);
94 
95  if (!sac_model_->doSamplesVerifyModel (indices_subset, model_coefficients, threshold_))
96  {
97  // Unfortunately we cannot "continue" after the first iteration, because k might not be set, while iterations gets incremented
98  if (k != 1.0)
99  {
100  ++iterations_;
101  continue;
102  }
103  }
104 
105  double d_cur_penalty = 0;
106  // Iterate through the 3d points and calculate the distances from them to the model
107  sac_model_->getDistancesToModel (model_coefficients, distances);
108 
109  if (distances.empty () && k > 1.0)
110  continue;
111 
112  for (const double &distance : distances)
113  d_cur_penalty += std::min (distance, threshold_);
114 
115  // Better match ?
116  if (d_cur_penalty < d_best_penalty)
117  {
118  d_best_penalty = d_cur_penalty;
119 
120  // Save the current model/coefficients selection as being the best so far
121  model_ = selection;
122  model_coefficients_ = model_coefficients;
123 
124  n_inliers_count = 0;
125  // Need to compute the number of inliers for this model to adapt k
126  for (const double &distance : distances)
127  if (distance <= threshold_)
128  n_inliers_count++;
129 
130  // Compute the k parameter (k=std::log(z)/std::log(1-w^n))
131  double w = static_cast<double> (n_inliers_count) / static_cast<double>(sac_model_->getIndices ()->size ());
132  double p_no_outliers = 1 - pow (w, static_cast<double> (selection.size ()));
133  p_no_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by -Inf
134  p_no_outliers = (std::min) (1 - std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by 0.
135  k = std::log (1 - probability_) / std::log (p_no_outliers);
136  }
137 
138  ++iterations_;
139  if (debug_verbosity_level > 1)
140  PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Trial %d out of %d. Best penalty is %f.\n", iterations_, static_cast<int> (std::ceil (k)), d_best_penalty);
141  if (iterations_ > max_iterations_)
142  {
143  if (debug_verbosity_level > 0)
144  PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] MSAC reached the maximum number of trials.\n");
145  break;
146  }
147  }
148 
149  if (model_.empty ())
150  {
151  if (debug_verbosity_level > 0)
152  PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Unable to find a solution!\n");
153  return (false);
154  }
155 
156  // Iterate through the 3d points and calculate the distances from them to the model again
157  sac_model_->getDistancesToModel (model_coefficients_, distances);
158  std::vector<int> &indices = *sac_model_->getIndices ();
159  if (distances.size () != indices.size ())
160  {
161  PCL_ERROR ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Estimated distances (%lu) differs than the normal of indices (%lu).\n", distances.size (), indices.size ());
162  return (false);
163  }
164 
165  inliers_.resize (distances.size ());
166  // Get the inliers for the best model found
167  n_inliers_count = 0;
168  for (std::size_t i = 0; i < distances.size (); ++i)
169  if (distances[i] <= threshold_)
170  inliers_[n_inliers_count++] = indices[i];
171 
172  // Resize the inliers vector
173  inliers_.resize (n_inliers_count);
174 
175  if (debug_verbosity_level > 0)
176  PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Model: %lu size, %d inliers.\n", model_.size (), n_inliers_count);
177 
178  return (true);
179 }
180 
181 #define PCL_INSTANTIATE_RandomizedMEstimatorSampleConsensus(T) template class PCL_EXPORTS pcl::RandomizedMEstimatorSampleConsensus<T>;
182 
183 #endif // PCL_SAMPLE_CONSENSUS_IMPL_RMSAC_H_
184 
#define pcl_lrint(x)
Definition: pcl_macros.h:168
float distance(const PointT &p1, const PointT &p2)
Definition: geometry.h:60
bool computeModel(int debug_verbosity_level=0) override
Compute the actual model and find the inliers.
Definition: rmsac.hpp:48