Point Cloud Library (PCL) 1.13.0
lmeds.h
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40
41#pragma once
42
43#include <pcl/sample_consensus/sac.h>
44#include <pcl/sample_consensus/sac_model.h>
45
46namespace pcl
47{
48 /** \brief @b LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm. LMedS
49 * is a RANSAC-like model-fitting algorithm that can tolerate up to 50% outliers without requiring thresholds to be
50 * set. See Andrea Fusiello's "Elements of Geometric Computer Vision"
51 * (http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/FUSIELLO4/tutorial.html#x1-520007) for more details.
52 * In contrast to RANSAC, LMedS does not divide the points into inliers and outliers when finding the model. Instead,
53 * it uses the median of all point-model distances as the measure of how good a model is. A threshold is only needed
54 * at the end, when it is determined which points belong to the found model.
55 * \author Radu B. Rusu
56 * \ingroup sample_consensus
57 */
58 template <typename PointT>
59 class LeastMedianSquares : public SampleConsensus<PointT>
60 {
61 using SampleConsensusModelPtr = typename SampleConsensusModel<PointT>::Ptr;
62
63 public:
64 using Ptr = shared_ptr<LeastMedianSquares<PointT> >;
65 using ConstPtr = shared_ptr<const LeastMedianSquares<PointT> >;
66
74
75 /** \brief LMedS (Least Median of Squares) main constructor
76 * \param[in] model a Sample Consensus model
77 */
78 LeastMedianSquares (const SampleConsensusModelPtr &model)
79 : SampleConsensus<PointT> (model)
80 {
81 // Maximum number of trials before we give up.
82 max_iterations_ = 50;
83 }
84
85 /** \brief LMedS (Least Median of Squares) main constructor
86 * \param[in] model a Sample Consensus model
87 * \param[in] threshold distance to model threshold
88 */
89 LeastMedianSquares (const SampleConsensusModelPtr &model, double threshold)
90 : SampleConsensus<PointT> (model, threshold)
91 {
92 // Maximum number of trials before we give up.
93 max_iterations_ = 50;
94 }
95
96 /** \brief Compute the actual model and find the inliers
97 * \param[in] debug_verbosity_level enable/disable on-screen debug information and set the verbosity level
98 */
99 bool
100 computeModel (int debug_verbosity_level = 0) override;
101 };
102}
103
104#ifdef PCL_NO_PRECOMPILE
105#include <pcl/sample_consensus/impl/lmeds.hpp>
106#endif
LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm.
Definition: lmeds.h:60
LeastMedianSquares(const SampleConsensusModelPtr &model)
LMedS (Least Median of Squares) main constructor.
Definition: lmeds.h:78
LeastMedianSquares(const SampleConsensusModelPtr &model, double threshold)
LMedS (Least Median of Squares) main constructor.
Definition: lmeds.h:89
shared_ptr< LeastMedianSquares< PointT > > Ptr
Definition: lmeds.h:64
shared_ptr< const LeastMedianSquares< PointT > > ConstPtr
Definition: lmeds.h:65
bool computeModel(int debug_verbosity_level=0) override
Compute the actual model and find the inliers.
Definition: lmeds.hpp:49
SampleConsensus represents the base class.
Definition: sac.h:61
Indices inliers_
The indices of the points that were chosen as inliers after the last computeModel () call.
Definition: sac.h:326
int iterations_
Total number of internal loop iterations that we've done so far.
Definition: sac.h:335
Indices model_
The model found after the last computeModel () as point cloud indices.
Definition: sac.h:323
Eigen::VectorXf model_coefficients_
The coefficients of our model computed directly from the model found.
Definition: sac.h:329
double threshold_
Distance to model threshold.
Definition: sac.h:338
SampleConsensusModelPtr sac_model_
The underlying data model used (i.e.
Definition: sac.h:320
int max_iterations_
Maximum number of iterations before giving up.
Definition: sac.h:341
shared_ptr< SampleConsensusModel< PointT > > Ptr
Definition: sac_model.h:77
A point structure representing Euclidean xyz coordinates, and the RGB color.