Point Cloud Library (PCL) 1.14.0
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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//////////////////////////////////////////////////////////////////////////
47template <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 const double log_probability = std::log (1.0 - probability_);
62 const double one_over_indices = 1.0 / static_cast<double> (sac_model_->getIndices ()->size ());
63
64 Indices selection;
65 Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
66 std::vector<double> distances;
67 std::set<index_t> indices_subset;
68
69 int n_inliers_count = 0;
70 unsigned skipped_count = 0;
71 // suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
72 const unsigned max_skip = max_iterations_ * 10;
73
74 // Number of samples to try randomly
75 std::size_t fraction_nr_points = pcl_lrint (static_cast<double>(sac_model_->getIndices ()->size ()) * fraction_nr_pretest_ / 100.0);
76
77 // Iterate
78 while (iterations_ < k && skipped_count < max_skip)
79 {
80 // Get X samples which satisfy the model criteria
81 sac_model_->getSamples (iterations_, selection);
82
83 if (selection.empty ()) break;
84
85 // Search for inliers in the point cloud for the current plane model M
86 if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
87 {
88 //iterations_++;
89 ++ skipped_count;
90 continue;
91 }
92
93 // RMSAC addon: verify a random fraction of the data
94 // Get X random samples which satisfy the model criterion
95 this->getRandomSamples (sac_model_->getIndices (), fraction_nr_points, indices_subset);
96
97 if (!sac_model_->doSamplesVerifyModel (indices_subset, model_coefficients, threshold_))
98 {
99 // Unfortunately we cannot "continue" after the first iteration, because k might not be set, while iterations gets incremented
100 if (k != 1.0)
101 {
102 ++iterations_;
103 continue;
104 }
105 }
106
107 double d_cur_penalty = 0;
108 // Iterate through the 3d points and calculate the distances from them to the model
109 sac_model_->getDistancesToModel (model_coefficients, distances);
110
111 if (distances.empty ())
112 {
113 ++ skipped_count;
114 continue;
115 }
116
117 for (const double &distance : distances)
118 d_cur_penalty += std::min (distance, threshold_);
119
120 // Better match ?
121 if (d_cur_penalty < d_best_penalty)
122 {
123 d_best_penalty = d_cur_penalty;
124
125 // Save the current model/coefficients selection as being the best so far
126 model_ = selection;
127 model_coefficients_ = model_coefficients;
128
129 n_inliers_count = 0;
130 // Need to compute the number of inliers for this model to adapt k
131 for (const double &distance : distances)
132 if (distance <= threshold_)
133 n_inliers_count++;
134
135 // Compute the k parameter (k=std::log(z)/std::log(1-w^n))
136 const double w = static_cast<double> (n_inliers_count) * one_over_indices;
137 double p_outliers = 1.0 - std::pow (w, static_cast<double> (selection.size ())); // Probability that selection is contaminated by at least one outlier
138 p_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_outliers); // Avoid division by -Inf
139 p_outliers = (std::min) (1.0 - std::numeric_limits<double>::epsilon (), p_outliers); // Avoid division by 0.
140 k = log_probability / std::log (p_outliers);
141 }
142
143 ++iterations_;
144 if (debug_verbosity_level > 1)
145 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);
146 if (iterations_ > max_iterations_)
147 {
148 if (debug_verbosity_level > 0)
149 PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] MSAC reached the maximum number of trials.\n");
150 break;
151 }
152 }
153
154 if (model_.empty ())
155 {
156 if (debug_verbosity_level > 0)
157 PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Unable to find a solution!\n");
158 return (false);
159 }
160
161 // Iterate through the 3d points and calculate the distances from them to the model again
162 sac_model_->getDistancesToModel (model_coefficients_, distances);
163 Indices &indices = *sac_model_->getIndices ();
164 if (distances.size () != indices.size ())
165 {
166 PCL_ERROR ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Estimated distances (%lu) differs than the normal of indices (%lu).\n", distances.size (), indices.size ());
167 return (false);
168 }
169
170 inliers_.resize (distances.size ());
171 // Get the inliers for the best model found
172 n_inliers_count = 0;
173 for (std::size_t i = 0; i < distances.size (); ++i)
174 if (distances[i] <= threshold_)
175 inliers_[n_inliers_count++] = indices[i];
176
177 // Resize the inliers vector
178 inliers_.resize (n_inliers_count);
179
180 if (debug_verbosity_level > 0)
181 PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Model: %lu size, %d inliers.\n", model_.size (), n_inliers_count);
182
183 return (true);
184}
185
186#define PCL_INSTANTIATE_RandomizedMEstimatorSampleConsensus(T) template class PCL_EXPORTS pcl::RandomizedMEstimatorSampleConsensus<T>;
187
188#endif // PCL_SAMPLE_CONSENSUS_IMPL_RMSAC_H_
189
bool computeModel(int debug_verbosity_level=0) override
Compute the actual model and find the inliers.
Definition rmsac.hpp:48
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
#define pcl_lrint(x)
Definition pcl_macros.h:253