lmeds.h
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#pragma once
#include <pcl/sample_consensus/sac.h>
#include <pcl/sample_consensus/sac_model.h>
namespace pcl
{
/** \brief @b LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm. LMedS
* is a RANSAC-like model-fitting algorithm that can tolerate up to 50% outliers without requiring thresholds to be
* set. See Andrea Fusiello's "Elements of Geometric Computer Vision"
* (http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/FUSIELLO4/tutorial.html#x1-520007) for more details.
* In contrast to RANSAC, LMedS does not divide the points into inliers and outliers when finding the model. Instead,
* it uses the median of all point-model distances as the measure of how good a model is. A threshold is only needed
* at the end, when it is determined which points belong to the found model.
* \author Radu B. Rusu
* \ingroup sample_consensus
*/
template <typename PointT>
class LeastMedianSquares : public SampleConsensus<PointT>
{
using SampleConsensusModelPtr = typename SampleConsensusModel<PointT>::Ptr;
public:
using Ptr = shared_ptr<LeastMedianSquares<PointT> >;
using ConstPtr = shared_ptr<const LeastMedianSquares<PointT> >;
using SampleConsensus<PointT>::max_iterations_;
using SampleConsensus<PointT>::threshold_;
using SampleConsensus<PointT>::iterations_;
using SampleConsensus<PointT>::sac_model_;
using SampleConsensus<PointT>::model_;
using SampleConsensus<PointT>::model_coefficients_;
using SampleConsensus<PointT>::inliers_;
/** \brief LMedS (Least Median of Squares) main constructor
* \param[in] model a Sample Consensus model
*/
LeastMedianSquares (const SampleConsensusModelPtr &model)
: SampleConsensus<PointT> (model)
{
// Maximum number of trials before we give up.
max_iterations_ = 50;
}
/** \brief LMedS (Least Median of Squares) main constructor
* \param[in] model a Sample Consensus model
* \param[in] threshold distance to model threshold
*/
LeastMedianSquares (const SampleConsensusModelPtr &model, double threshold)
: SampleConsensus<PointT> (model, threshold)
{
// Maximum number of trials before we give up.
max_iterations_ = 50;
}
/** \brief Compute the actual model and find the inliers
* \param[in] debug_verbosity_level enable/disable on-screen debug information and set the verbosity level
*/
bool
computeModel (int debug_verbosity_level = 0) override;
};
}
#ifdef PCL_NO_PRECOMPILE
#include <pcl/sample_consensus/impl/lmeds.hpp>
#endif
pcl::LeastMedianSquares
LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm.
Definition: lmeds.h:60
pcl::LeastMedianSquares::LeastMedianSquares
LeastMedianSquares(const SampleConsensusModelPtr &model)
LMedS (Least Median of Squares) main constructor.
Definition: lmeds.h:78
pcl::LeastMedianSquares::LeastMedianSquares
LeastMedianSquares(const SampleConsensusModelPtr &model, double threshold)
LMedS (Least Median of Squares) main constructor.
Definition: lmeds.h:89
pcl::LeastMedianSquares::Ptr
shared_ptr< LeastMedianSquares< PointT > > Ptr
Definition: lmeds.h:64
pcl::LeastMedianSquares::ConstPtr
shared_ptr< const LeastMedianSquares< PointT > > ConstPtr
Definition: lmeds.h:65
pcl::LeastMedianSquares::computeModel
bool computeModel(int debug_verbosity_level=0) override
Compute the actual model and find the inliers.
Definition: lmeds.hpp:49
pcl::SampleConsensus
SampleConsensus represents the base class.
Definition: sac.h:61
pcl::SampleConsensus< PointT >::max_iterations_
int max_iterations_
Maximum number of iterations before giving up.
Definition: sac.h:341
pcl::SampleConsensusModel::Ptr
shared_ptr< SampleConsensusModel< PointT > > Ptr
Definition: sac_model.h:77
pcl
Definition: convolution.h:46
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:675