Point Cloud Library (PCL)  1.12.0-dev
msac.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 
46 namespace 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
pcl
Definition: convolution.h:46
pcl::MEstimatorSampleConsensus::Ptr
shared_ptr< MEstimatorSampleConsensus< PointT > > Ptr
Definition: msac.h:65
pcl::MEstimatorSampleConsensus
MEstimatorSampleConsensus represents an implementation of the MSAC (M-estimator SAmple Consensus) alg...
Definition: msac.h:60
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:674
pcl::MEstimatorSampleConsensus::MEstimatorSampleConsensus
MEstimatorSampleConsensus(const SampleConsensusModelPtr &model, double threshold)
MSAC (M-estimator SAmple Consensus) main constructor.
Definition: msac.h:91
pcl::MEstimatorSampleConsensus::computeModel
bool computeModel(int debug_verbosity_level=0) override
Compute the actual model and find the inliers.
Definition: msac.hpp:48
pcl::SampleConsensusModel::Ptr
shared_ptr< SampleConsensusModel< PointT > > Ptr
Definition: sac_model.h:77
pcl::MEstimatorSampleConsensus::MEstimatorSampleConsensus
MEstimatorSampleConsensus(const SampleConsensusModelPtr &model)
MSAC (M-estimator SAmple Consensus) main constructor.
Definition: msac.h:80
pcl::SampleConsensus
SampleConsensus represents the base class.
Definition: sac.h:60
pcl::MEstimatorSampleConsensus::ConstPtr
shared_ptr< const MEstimatorSampleConsensus< PointT > > ConstPtr
Definition: msac.h:66
pcl::SampleConsensus< PointT >::max_iterations_
int max_iterations_
Maximum number of iterations before giving up.
Definition: sac.h:341