Point Cloud Library (PCL) 1.15.0
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msac.h
1/*
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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 MEstimatorSampleConsensus represents an implementation of the MSAC (M-estimator SAmple Consensus)
49 * algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S.
50 * Torr and A. Zisserman, Computer Vision and Image Understanding, vol 78, 2000.
51 * The difference to RANSAC is how the quality of a model is computed: RANSAC counts the number of inliers, given a
52 * threshold. The more inliers, the better the model is - it does not matter how close the inliers actually are to
53 * the model, as long as they are within the threshold. MSAC changes this by using the sum of all point-model distances
54 * as the quality measure, however outliers only add the threshold instead of their true distance. This method can lead
55 * to better results compared to RANSAC.
56 * \author Radu B. Rusu
57 * \ingroup sample_consensus
58 */
59 template <typename PointT>
61 {
62 using SampleConsensusModelPtr = typename SampleConsensusModel<PointT>::Ptr;
63
64 public:
65 using Ptr = shared_ptr<MEstimatorSampleConsensus<PointT> >;
66 using ConstPtr = shared_ptr<const MEstimatorSampleConsensus<PointT> >;
67
76
77 /** \brief MSAC (M-estimator SAmple Consensus) main constructor
78 * \param[in] model a Sample Consensus model
79 */
80 MEstimatorSampleConsensus (const SampleConsensusModelPtr &model)
81 : SampleConsensus<PointT> (model)
82 {
83 // Maximum number of trials before we give up.
84 max_iterations_ = 10000;
85 }
86
87 /** \brief MSAC (M-estimator SAmple Consensus) main constructor
88 * \param[in] model a Sample Consensus model
89 * \param[in] threshold distance to model threshold
90 */
91 MEstimatorSampleConsensus (const SampleConsensusModelPtr &model, double threshold)
92 : SampleConsensus<PointT> (model, threshold)
93 {
94 // Maximum number of trials before we give up.
95 max_iterations_ = 10000;
96 }
97
98 /** \brief Compute the actual model and find the inliers
99 * \param[in] debug_verbosity_level enable/disable on-screen debug information and set the verbosity level
100 */
101 bool
102 computeModel (int debug_verbosity_level = 0) override;
103 };
104}
105
106#ifdef PCL_NO_PRECOMPILE
107#include <pcl/sample_consensus/impl/msac.hpp>
108#endif
MEstimatorSampleConsensus represents an implementation of the MSAC (M-estimator SAmple Consensus) alg...
Definition msac.h:61
MEstimatorSampleConsensus(const SampleConsensusModelPtr &model, double threshold)
MSAC (M-estimator SAmple Consensus) main constructor.
Definition msac.h:91
shared_ptr< const MEstimatorSampleConsensus< PointT > > ConstPtr
Definition msac.h:66
MEstimatorSampleConsensus(const SampleConsensusModelPtr &model)
MSAC (M-estimator SAmple Consensus) main constructor.
Definition msac.h:80
bool computeModel(int debug_verbosity_level=0) override
Compute the actual model and find the inliers.
Definition msac.hpp:48
shared_ptr< MEstimatorSampleConsensus< PointT > > Ptr
Definition msac.h:65
SampleConsensus represents the base class.
Definition sac.h:61
double probability_
Desired probability of choosing at least one sample free from outliers.
Definition sac.h:332
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:78
A point structure representing Euclidean xyz coordinates, and the RGB color.