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rransac.hpp
/*
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_RRANSAC_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_RRANSAC_H_
#include <pcl/sample_consensus/rransac.h>
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::RandomizedRandomSampleConsensus<PointT>::computeModel (int debug_verbosity_level)
{
// Warn and exit if no threshold was set
if (threshold_ == std::numeric_limits<double>::max())
{
PCL_ERROR ("[pcl::RandomizedRandomSampleConsensus::computeModel] No threshold set!\n");
return (false);
}
iterations_ = 0;
std::size_t n_best_inliers_count = 0;
double k = std::numeric_limits<double>::max();
Indices selection;
Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
std::set<index_t> indices_subset;
const double log_probability = std::log (1.0 - probability_);
const double one_over_indices = 1.0 / static_cast<double> (sac_model_->getIndices ()->size ());
std::size_t n_inliers_count;
unsigned skipped_count = 0;
// suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
const unsigned max_skip = max_iterations_ * 10;
// Number of samples to try randomly
const std::size_t fraction_nr_points = pcl_lrint (static_cast<double>(sac_model_->getIndices ()->size ()) * fraction_nr_pretest_ / 100.0);
// Iterate
while (iterations_ < k)
{
// Get X samples which satisfy the model criteria
sac_model_->getSamples (iterations_, selection);
if (selection.empty ())
{
PCL_ERROR ("[pcl::RandomizedRandomSampleConsensus::computeModel] No samples could be selected!\n");
break;
}
// Search for inliers in the point cloud for the current plane model M
if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
{
//iterations_++;
++skipped_count;
if (skipped_count < max_skip)
{
PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] The function computeModelCoefficients failed, so continue with next iteration.\n");
continue;
}
else
{
PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] The function computeModelCoefficients failed, and RRANSAC reached the maximum number of trials.\n");
break;
}
}
// RRANSAC addon: verify a random fraction of the data
// Get X random samples which satisfy the model criterion
this->getRandomSamples (sac_model_->getIndices (), fraction_nr_points, indices_subset);
if (!sac_model_->doSamplesVerifyModel (indices_subset, model_coefficients, threshold_))
{
++iterations_;
PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] The function doSamplesVerifyModel failed, so continue with next iteration.\n");
continue;
}
// Select the inliers that are within threshold_ from the model
n_inliers_count = sac_model_->countWithinDistance (model_coefficients, threshold_);
// Better match ?
if (n_inliers_count > n_best_inliers_count)
{
n_best_inliers_count = n_inliers_count;
// Save the current model/inlier/coefficients selection as being the best so far
model_ = selection;
model_coefficients_ = model_coefficients;
// Compute the k parameter (k=std::log(z)/std::log(1-w^n))
const double w = static_cast<double> (n_best_inliers_count) * one_over_indices;
double p_outliers = 1.0 - std::pow (w, static_cast<double> (selection.size ())); // Probability that selection is contaminated by at least one outlier
p_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_outliers); // Avoid division by -Inf
p_outliers = (std::min) (1.0 - std::numeric_limits<double>::epsilon (), p_outliers); // Avoid division by 0.
k = log_probability / std::log (p_outliers);
}
++iterations_;
if (debug_verbosity_level > 1)
PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] Trial %d out of %d: %u inliers (best is: %u so far).\n", iterations_, static_cast<int> (std::ceil (k)), n_inliers_count, n_best_inliers_count);
if (iterations_ > max_iterations_)
{
if (debug_verbosity_level > 0)
PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] RRANSAC reached the maximum number of trials.\n");
break;
}
}
if (debug_verbosity_level > 0)
PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] Model: %lu size, %u inliers.\n", model_.size (), n_best_inliers_count);
if (model_.empty ())
{
PCL_ERROR ("[pcl::RandomizedRandomSampleConsensus::computeModel] RRANSAC found no model.\n");
inliers_.clear ();
return (false);
}
// Get the set of inliers that correspond to the best model found so far
sac_model_->selectWithinDistance (model_coefficients_, threshold_, inliers_);
return (true);
}
#define PCL_INSTANTIATE_RandomizedRandomSampleConsensus(T) template class PCL_EXPORTS pcl::RandomizedRandomSampleConsensus<T>;
#endif // PCL_SAMPLE_CONSENSUS_IMPL_RRANSAC_H_
pcl::RandomizedRandomSampleConsensus::computeModel
bool computeModel(int debug_verbosity_level=0) override
Compute the actual model and find the inliers.
Definition rransac.hpp:48
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
pcl_lrint
#define pcl_lrint(x)
Definition pcl_macros.h:253