Point Cloud Library (PCL) 1.12.0
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msac.hpp
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40
41#ifndef PCL_SAMPLE_CONSENSUS_IMPL_MSAC_H_
42#define PCL_SAMPLE_CONSENSUS_IMPL_MSAC_H_
43
44#include <pcl/sample_consensus/msac.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::MEstimatorSampleConsensus::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 Indices selection;
62 Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
63 std::vector<double> distances;
64
65 int n_inliers_count = 0;
66 unsigned skipped_count = 0;
67 // suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
68 const unsigned max_skip = max_iterations_ * 10;
69
70 // Iterate
71 while (iterations_ < k && skipped_count < max_skip)
72 {
73 // Get X samples which satisfy the model criteria
74 sac_model_->getSamples (iterations_, selection);
75
76 if (selection.empty ()) break;
77
78 // Search for inliers in the point cloud for the current plane model M
79 if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
80 {
81 //iterations_++;
82 ++ skipped_count;
83 continue;
84 }
85
86 double d_cur_penalty = 0;
87 // Iterate through the 3d points and calculate the distances from them to the model
88 sac_model_->getDistancesToModel (model_coefficients, distances);
89
90 if (distances.empty () && k > 1.0)
91 continue;
92
93 for (const double &distance : distances)
94 d_cur_penalty += (std::min) (distance, threshold_);
95
96 // Better match ?
97 if (d_cur_penalty < d_best_penalty)
98 {
99 d_best_penalty = d_cur_penalty;
100
101 // Save the current model/coefficients selection as being the best so far
102 model_ = selection;
103 model_coefficients_ = model_coefficients;
104
105 n_inliers_count = 0;
106 // Need to compute the number of inliers for this model to adapt k
107 for (const double &distance : distances)
108 if (distance <= threshold_)
109 ++n_inliers_count;
110
111 // Compute the k parameter (k=std::log(z)/std::log(1-w^n))
112 double w = static_cast<double> (n_inliers_count) / static_cast<double> (sac_model_->getIndices ()->size ());
113 double p_no_outliers = 1.0 - std::pow (w, static_cast<double> (selection.size ()));
114 p_no_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by -Inf
115 p_no_outliers = (std::min) (1.0 - std::numeric_limits<double>::epsilon (), p_no_outliers); // Avoid division by 0.
116 k = std::log (1.0 - probability_) / std::log (p_no_outliers);
117 }
118
119 ++iterations_;
120 if (debug_verbosity_level > 1)
121 PCL_DEBUG ("[pcl::MEstimatorSampleConsensus::computeModel] Trial %d out of %d. Best penalty is %f.\n", iterations_, static_cast<int> (std::ceil (k)), d_best_penalty);
122 if (iterations_ > max_iterations_)
123 {
124 if (debug_verbosity_level > 0)
125 PCL_DEBUG ("[pcl::MEstimatorSampleConsensus::computeModel] MSAC reached the maximum number of trials.\n");
126 break;
127 }
128 }
129
130 if (model_.empty ())
131 {
132 if (debug_verbosity_level > 0)
133 PCL_DEBUG ("[pcl::MEstimatorSampleConsensus::computeModel] Unable to find a solution!\n");
134 return (false);
135 }
136
137 // Iterate through the 3d points and calculate the distances from them to the model again
138 sac_model_->getDistancesToModel (model_coefficients_, distances);
139 Indices &indices = *sac_model_->getIndices ();
140
141 if (distances.size () != indices.size ())
142 {
143 PCL_ERROR ("[pcl::MEstimatorSampleConsensus::computeModel] Estimated distances (%lu) differs than the normal of indices (%lu).\n", distances.size (), indices.size ());
144 return (false);
145 }
146
147 inliers_.resize (distances.size ());
148 // Get the inliers for the best model found
149 n_inliers_count = 0;
150 for (std::size_t i = 0; i < distances.size (); ++i)
151 if (distances[i] <= threshold_)
152 inliers_[n_inliers_count++] = indices[i];
153
154 // Resize the inliers vector
155 inliers_.resize (n_inliers_count);
156
157 if (debug_verbosity_level > 0)
158 PCL_DEBUG ("[pcl::MEstimatorSampleConsensus::computeModel] Model: %lu size, %d inliers.\n", model_.size (), n_inliers_count);
159
160 return (true);
161}
162
163#define PCL_INSTANTIATE_MEstimatorSampleConsensus(T) template class PCL_EXPORTS pcl::MEstimatorSampleConsensus<T>;
164
165#endif // PCL_SAMPLE_CONSENSUS_IMPL_MSAC_H_
bool computeModel(int debug_verbosity_level=0) override
Compute the actual model and find the inliers.
Definition msac.hpp:48
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133