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Point Cloud Library (PCL)
1.9.1-dev
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MEstimatorSampleConsensus represents an implementation of the MSAC (M-estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S. More...
#include <pcl/sample_consensus/msac.h>
Inheritance diagram for pcl::MEstimatorSampleConsensus< PointT >:Public Types | |
| typedef boost::shared_ptr< MEstimatorSampleConsensus > | Ptr |
| typedef boost::shared_ptr< const MEstimatorSampleConsensus > | ConstPtr |
| Public Types inherited from pcl::SampleConsensus< PointT > | |
| typedef boost::shared_ptr< SampleConsensus > | Ptr |
| typedef boost::shared_ptr< const SampleConsensus > | ConstPtr |
Public Member Functions | |
| MEstimatorSampleConsensus (const SampleConsensusModelPtr &model) | |
| MSAC (M-estimator SAmple Consensus) main constructor. More... | |
| MEstimatorSampleConsensus (const SampleConsensusModelPtr &model, double threshold) | |
| MSAC (M-estimator SAmple Consensus) main constructor. More... | |
| bool | computeModel (int debug_verbosity_level=0) override |
| Compute the actual model and find the inliers. More... | |
| Public Member Functions inherited from pcl::SampleConsensus< PointT > | |
| SampleConsensus (const SampleConsensusModelPtr &model, bool random=false) | |
| Constructor for base SAC. More... | |
| SampleConsensus (const SampleConsensusModelPtr &model, double threshold, bool random=false) | |
| Constructor for base SAC. More... | |
| void | setSampleConsensusModel (const SampleConsensusModelPtr &model) |
| Set the Sample Consensus model to use. More... | |
| SampleConsensusModelPtr | getSampleConsensusModel () const |
| Get the Sample Consensus model used. More... | |
| virtual | ~SampleConsensus () |
| Destructor for base SAC. More... | |
| void | setDistanceThreshold (double threshold) |
| Set the distance to model threshold. More... | |
| double | getDistanceThreshold () const |
| Get the distance to model threshold, as set by the user. More... | |
| void | setMaxIterations (int max_iterations) |
| Set the maximum number of iterations. More... | |
| int | getMaxIterations () const |
| Get the maximum number of iterations, as set by the user. More... | |
| void | setProbability (double probability) |
| Set the desired probability of choosing at least one sample free from outliers. More... | |
| double | getProbability () const |
| Obtain the probability of choosing at least one sample free from outliers, as set by the user. More... | |
| virtual bool | refineModel (const double sigma=3.0, const unsigned int max_iterations=1000) |
| Refine the model found. More... | |
| void | getRandomSamples (const boost::shared_ptr< std::vector< int > > &indices, size_t nr_samples, std::set< int > &indices_subset) |
| Get a set of randomly selected indices. More... | |
| void | getModel (std::vector< int > &model) const |
| Return the best model found so far. More... | |
| void | getInliers (std::vector< int > &inliers) const |
| Return the best set of inliers found so far for this model. More... | |
| void | getModelCoefficients (Eigen::VectorXf &model_coefficients) const |
| Return the model coefficients of the best model found so far. More... | |
Additional Inherited Members | |
| Protected Member Functions inherited from pcl::SampleConsensus< PointT > | |
| double | rnd () |
| Boost-based random number generator. More... | |
| Protected Attributes inherited from pcl::SampleConsensus< PointT > | |
| SampleConsensusModelPtr | sac_model_ |
| The underlying data model used (i.e. More... | |
| std::vector< int > | model_ |
| The model found after the last computeModel () as point cloud indices. More... | |
| std::vector< int > | inliers_ |
| The indices of the points that were chosen as inliers after the last computeModel () call. More... | |
| Eigen::VectorXf | model_coefficients_ |
| The coefficients of our model computed directly from the model found. More... | |
| double | probability_ |
| Desired probability of choosing at least one sample free from outliers. More... | |
| int | iterations_ |
| Total number of internal loop iterations that we've done so far. More... | |
| double | threshold_ |
| Distance to model threshold. More... | |
| int | max_iterations_ |
| Maximum number of iterations before giving up. More... | |
| boost::mt19937 | rng_alg_ |
| Boost-based random number generator algorithm. More... | |
| boost::shared_ptr< boost::uniform_01< boost::mt19937 > > | rng_ |
| Boost-based random number generator distribution. More... | |
MEstimatorSampleConsensus represents an implementation of the MSAC (M-estimator SAmple Consensus) algorithm, as described in: "MLESAC: A new robust estimator with application to estimating image geometry", P.H.S.
Torr and A. Zisserman, Computer Vision and Image Understanding, vol 78, 2000.
| typedef boost::shared_ptr<const MEstimatorSampleConsensus> pcl::MEstimatorSampleConsensus< PointT >::ConstPtr |
| typedef boost::shared_ptr<MEstimatorSampleConsensus> pcl::MEstimatorSampleConsensus< PointT >::Ptr |
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inline |
MSAC (M-estimator SAmple Consensus) main constructor.
| [in] | model | a Sample Consensus model |
Definition at line 76 of file msac.h.
References pcl::SampleConsensus< PointT >::max_iterations_.
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inline |
MSAC (M-estimator SAmple Consensus) main constructor.
| [in] | model | a Sample Consensus model |
| [in] | threshold | distance to model threshold |
Definition at line 87 of file msac.h.
References pcl::MEstimatorSampleConsensus< PointT >::computeModel(), and pcl::SampleConsensus< PointT >::max_iterations_.
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overridevirtual |
Compute the actual model and find the inliers.
| [in] | debug_verbosity_level | enable/disable on-screen debug information and set the verbosity level |
Implements pcl::SampleConsensus< PointT >.
Definition at line 48 of file msac.hpp.
Referenced by pcl::MEstimatorSampleConsensus< PointT >::MEstimatorSampleConsensus().
