W3cubDocs
/PointCloudLibraryRandomizedMEstimatorSampleConsensus represents an implementation of the RMSAC (Randomized M-estimator SAmple Consensus) algorithm, which basically adds a Td,d test (see RandomizedRandomSampleConsensus) to an MSAC estimator (see MEstimatorSampleConsensus). More...
#include <pcl/sample_consensus/rmsac.h>
Public Types | |
| using | Ptr = shared_ptr< RandomizedMEstimatorSampleConsensus< PointT > > |
| using | ConstPtr = shared_ptr< const RandomizedMEstimatorSampleConsensus< PointT > > |
 Public Types inherited from pcl::SampleConsensus< PointT >
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| using | Ptr = shared_ptr< SampleConsensus< PointT > > |
| using | ConstPtr = shared_ptr< const SampleConsensus< PointT > > |
Public Member Functions | |
| RandomizedMEstimatorSampleConsensus (const SampleConsensusModelPtr &model) | |
| RMSAC (Randomized M-estimator SAmple Consensus) main constructor. More... |
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| RandomizedMEstimatorSampleConsensus (const SampleConsensusModelPtr &model, double threshold) | |
| RMSAC (Randomized M-estimator SAmple Consensus) main constructor. More... |
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| bool | computeModel (int debug_verbosity_level=0) override |
| Compute the actual model and find the inliers. More... |
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| void | setFractionNrPretest (double nr_pretest) |
| Set the percentage of points to pre-test. More... |
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| double | getFractionNrPretest () const |
| Get the percentage of points to pre-test. More... |
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Public Member Functions inherited from pcl::SampleConsensus< PointT >
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| SampleConsensus (const SampleConsensusModelPtr &model, bool random=false) | |
| Constructor for base SAC. More... |
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| SampleConsensus (const SampleConsensusModelPtr &model, double threshold, bool random=false) | |
| Constructor for base SAC. More... |
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| void | setSampleConsensusModel (const SampleConsensusModelPtr &model) |
| Set the Sample Consensus model to use. More... |
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| SampleConsensusModelPtr | getSampleConsensusModel () const |
| Get the Sample Consensus model used. More... |
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| virtual | ~SampleConsensus () |
| Destructor for base SAC. More... |
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| void | setDistanceThreshold (double threshold) |
| Set the distance to model threshold. More... |
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| double | getDistanceThreshold () const |
| Get the distance to model threshold, as set by the user. More... |
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| void | setMaxIterations (int max_iterations) |
| Set the maximum number of iterations. More... |
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| int | getMaxIterations () const |
| Get the maximum number of iterations, as set by the user. More... |
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| void | setProbability (double probability) |
| Set the desired probability of choosing at least one sample free from outliers. More... |
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| double | getProbability () const |
| Obtain the probability of choosing at least one sample free from outliers, as set by the user. More... |
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| void | setNumberOfThreads (const int nr_threads=-1) |
| Set the number of threads to use or turn off parallelization. More... |
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| int | getNumberOfThreads () const |
| Get the number of threads, as set by the user. More... |
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| virtual bool | refineModel (const double sigma=3.0, const unsigned int max_iterations=1000) |
| Refine the model found. More... |
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| void | getRandomSamples (const IndicesPtr &indices, std::size_t nr_samples, std::set< index_t > &indices_subset) |
| Get a set of randomly selected indices. More... |
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| void | getModel (Indices &model) const |
| Return the best model found so far. More... |
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| void | getInliers (Indices &inliers) const |
| Return the best set of inliers found so far for this model. More... |
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| void | getModelCoefficients (Eigen::VectorXf &model_coefficients) const |
| Return the model coefficients of the best model found so far. More... |
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Additional Inherited Members | |
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Protected Member Functions inherited from pcl::SampleConsensus< PointT >
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| double | rnd () |
| Boost-based random number generator. More... |
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Protected Attributes inherited from pcl::SampleConsensus< PointT >
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| SampleConsensusModelPtr | sac_model_ |
| The underlying data model used (i.e. More... |
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| Indices | model_ |
| The model found after the last computeModel () as point cloud indices. More... |
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| Indices | inliers_ |
| The indices of the points that were chosen as inliers after the last computeModel () call. More... |
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| Eigen::VectorXf | model_coefficients_ |
| The coefficients of our model computed directly from the model found. More... |
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| double | probability_ |
| Desired probability of choosing at least one sample free from outliers. More... |
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| int | iterations_ |
| Total number of internal loop iterations that we've done so far. More... |
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| double | threshold_ |
| Distance to model threshold. More... |
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| int | max_iterations_ |
| Maximum number of iterations before giving up. More... |
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| int | threads_ |
| The number of threads the scheduler should use, or a negative number if no parallelization is wanted. More... |
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| boost::mt19937 | rng_alg_ |
| Boost-based random number generator algorithm. More... |
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| std::shared_ptr< boost::uniform_01< boost::mt19937 > > | rng_ |
| Boost-based random number generator distribution. More... |
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Detailed Description
template<typename PointT>
class pcl::RandomizedMEstimatorSampleConsensus< PointT >
RandomizedMEstimatorSampleConsensus represents an implementation of the RMSAC (Randomized M-estimator SAmple Consensus) algorithm, which basically adds a Td,d test (see RandomizedRandomSampleConsensus) to an MSAC estimator (see MEstimatorSampleConsensus).
- Note
- RMSAC is useful in situations where most of the data samples belong to the model, and a fast outlier rejection algorithm is needed.
Member Typedef Documentation
ConstPtr
template<typename PointT >
| using pcl::RandomizedMEstimatorSampleConsensus< PointT >::ConstPtr = shared_ptr<const RandomizedMEstimatorSampleConsensus<PointT> > |
Ptr
template<typename PointT >
| using pcl::RandomizedMEstimatorSampleConsensus< PointT >::Ptr = shared_ptr<RandomizedMEstimatorSampleConsensus<PointT> > |
Constructor & Destructor Documentation
RandomizedMEstimatorSampleConsensus() [1/2]
template<typename PointT >
| inline |
RMSAC (Randomized M-estimator SAmple Consensus) main constructor.
- Parameters
-
[in] model a Sample Consensus model
Definition at line 76 of file rmsac.h.
References pcl::SampleConsensus< PointT >::max_iterations_.
RandomizedMEstimatorSampleConsensus() [2/2]
template<typename PointT >
| inline |
RMSAC (Randomized M-estimator SAmple Consensus) main constructor.
- Parameters
-
[in] model a Sample Consensus model [in] threshold distance to model threshold
Definition at line 88 of file rmsac.h.
References pcl::SampleConsensus< PointT >::max_iterations_.
Member Function Documentation
computeModel()
template<typename PointT >
| overridevirtual |
Compute the actual model and find the inliers.
- Parameters
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[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 rmsac.hpp.
References pcl::geometry::distance(), and pcl_lrint.
getFractionNrPretest()
template<typename PointT >
| inline |
setFractionNrPretest()
template<typename PointT >
| inline |
The documentation for this class was generated from the following files:
© 2009–2012, Willow Garage, Inc.
© 2012–, Open Perception, Inc.
Licensed under the BSD License.
https://pointclouds.org/documentation/classpcl_1_1_randomized_m_estimator_sample_consensus.html