lmeds.hpp
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_LMEDS_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_LMEDS_H_
#include <pcl/sample_consensus/lmeds.h>
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::LeastMedianSquares<PointT>::computeModel (int debug_verbosity_level)
{
// Warn and exit if no threshold was set
if (threshold_ == std::numeric_limits<double>::max())
{
PCL_ERROR ("[pcl::LeastMedianSquares::computeModel] No threshold set!\n");
return (false);
}
iterations_ = 0;
double d_best_penalty = std::numeric_limits<double>::max();
std::vector<int> best_model;
std::vector<int> selection;
Eigen::VectorXf model_coefficients;
std::vector<double> distances;
int n_inliers_count = 0;
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;
// Iterate
while (iterations_ < max_iterations_ && skipped_count < max_skip)
{
// Get X samples which satisfy the model criteria
sac_model_->getSamples (iterations_, selection);
if (selection.empty ()) break;
// Search for inliers in the point cloud for the current plane model M
if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
{
//iterations_++;
++skipped_count;
continue;
}
double d_cur_penalty = 0;
// d_cur_penalty = sum (min (dist, threshold))
// Iterate through the 3d points and calculate the distances from them to the model
sac_model_->getDistancesToModel (model_coefficients, distances);
// No distances? The model must not respect the user given constraints
if (distances.empty ())
{
//iterations_++;
++skipped_count;
continue;
}
std::sort (distances.begin (), distances.end ());
// d_cur_penalty = median (distances)
std::size_t mid = sac_model_->getIndices ()->size () / 2;
if (mid >= distances.size ())
{
//iterations_++;
++skipped_count;
continue;
}
// Do we have a "middle" point or should we "estimate" one ?
if (sac_model_->getIndices ()->size () % 2 == 0)
d_cur_penalty = (sqrt (distances[mid-1]) + sqrt (distances[mid])) / 2;
else
d_cur_penalty = sqrt (distances[mid]);
// Better match ?
if (d_cur_penalty < d_best_penalty)
{
d_best_penalty = d_cur_penalty;
// Save the current model/coefficients selection as being the best so far
model_ = selection;
model_coefficients_ = model_coefficients;
}
++iterations_;
if (debug_verbosity_level > 1)
PCL_DEBUG ("[pcl::LeastMedianSquares::computeModel] Trial %d out of %d. Best penalty is %f.\n", iterations_, max_iterations_, d_best_penalty);
}
if (model_.empty ())
{
if (debug_verbosity_level > 0)
PCL_DEBUG ("[pcl::LeastMedianSquares::computeModel] Unable to find a solution!\n");
return (false);
}
// Classify the data points into inliers and outliers
// Sigma = 1.4826 * (1 + 5 / (n-d)) * sqrt (M)
// @note: See "Robust Regression Methods for Computer Vision: A Review"
//double sigma = 1.4826 * (1 + 5 / (sac_model_->getIndices ()->size () - best_model.size ())) * sqrt (d_best_penalty);
//double threshold = 2.5 * sigma;
// Iterate through the 3d points and calculate the distances from them to the model again
sac_model_->getDistancesToModel (model_coefficients_, distances);
// No distances? The model must not respect the user given constraints
if (distances.empty ())
{
PCL_ERROR ("[pcl::LeastMedianSquares::computeModel] The model found failed to verify against the given constraints!\n");
return (false);
}
std::vector<int> &indices = *sac_model_->getIndices ();
if (distances.size () != indices.size ())
{
PCL_ERROR ("[pcl::LeastMedianSquares::computeModel] Estimated distances (%lu) differs than the normal of indices (%lu).\n", distances.size (), indices.size ());
return (false);
}
inliers_.resize (distances.size ());
// Get the inliers for the best model found
n_inliers_count = 0;
for (std::size_t i = 0; i < distances.size (); ++i)
if (distances[i] <= threshold_)
inliers_[n_inliers_count++] = indices[i];
// Resize the inliers vector
inliers_.resize (n_inliers_count);
if (debug_verbosity_level > 0)
PCL_DEBUG ("[pcl::LeastMedianSquares::computeModel] Model: %lu size, %d inliers.\n", model_.size (), n_inliers_count);
return (true);
}
#define PCL_INSTANTIATE_LeastMedianSquares(T) template class PCL_EXPORTS pcl::LeastMedianSquares<T>;
#endif // PCL_SAMPLE_CONSENSUS_IMPL_LMEDS_H_
pcl::LeastMedianSquares::computeModel
bool computeModel(int debug_verbosity_level=0) override
Compute the actual model and find the inliers.
Definition: lmeds.hpp:48