41 #ifndef PCL_SAMPLE_CONSENSUS_IMPL_MSAC_H_
42 #define PCL_SAMPLE_CONSENSUS_IMPL_MSAC_H_
44 #include <pcl/sample_consensus/msac.h>
47 template <
typename Po
intT>
bool
51 if (threshold_ == std::numeric_limits<double>::max())
53 PCL_ERROR (
"[pcl::MEstimatorSampleConsensus::computeModel] No threshold set!\n");
58 double d_best_penalty = std::numeric_limits<double>::max();
62 Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
63 std::vector<double> distances;
65 int n_inliers_count = 0;
66 unsigned skipped_count = 0;
68 const unsigned max_skip = max_iterations_ * 10;
71 while (iterations_ < k && skipped_count < max_skip)
74 sac_model_->getSamples (iterations_, selection);
76 if (selection.empty ())
break;
79 if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
86 double d_cur_penalty = 0;
88 sac_model_->getDistancesToModel (model_coefficients, distances);
90 if (distances.empty () && k > 1.0)
93 for (
const double &
distance : distances)
94 d_cur_penalty += (std::min) (
distance, threshold_);
97 if (d_cur_penalty < d_best_penalty)
99 d_best_penalty = d_cur_penalty;
103 model_coefficients_ = model_coefficients;
107 for (
const double &
distance : distances)
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);
115 p_no_outliers = (std::min) (1.0 - std::numeric_limits<double>::epsilon (), p_no_outliers);
116 k = std::log (1.0 - probability_) / std::log (p_no_outliers);
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_)
124 if (debug_verbosity_level > 0)
125 PCL_DEBUG (
"[pcl::MEstimatorSampleConsensus::computeModel] MSAC reached the maximum number of trials.\n");
132 if (debug_verbosity_level > 0)
133 PCL_DEBUG (
"[pcl::MEstimatorSampleConsensus::computeModel] Unable to find a solution!\n");
138 sac_model_->getDistancesToModel (model_coefficients_, distances);
139 Indices &indices = *sac_model_->getIndices ();
141 if (distances.size () != indices.size ())
143 PCL_ERROR (
"[pcl::MEstimatorSampleConsensus::computeModel] Estimated distances (%lu) differs than the normal of indices (%lu).\n", distances.size (), indices.size ());
147 inliers_.resize (distances.size ());
150 for (std::size_t i = 0; i < distances.size (); ++i)
151 if (distances[i] <= threshold_)
152 inliers_[n_inliers_count++] = indices[i];
155 inliers_.resize (n_inliers_count);
157 if (debug_verbosity_level > 0)
158 PCL_DEBUG (
"[pcl::MEstimatorSampleConsensus::computeModel] Model: %lu size, %d inliers.\n", model_.size (), n_inliers_count);
163 #define PCL_INSTANTIATE_MEstimatorSampleConsensus(T) template class PCL_EXPORTS pcl::MEstimatorSampleConsensus<T>;
bool computeModel(int debug_verbosity_level=0) override
Compute the actual model and find the inliers.
float distance(const PointT &p1, const PointT &p2)
IndicesAllocator<> Indices
Type used for indices in PCL.