W3cubDocs
/PointCloudLibraryRandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RANdom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O. More...
#include <pcl/sample_consensus/rransac.h>
Public Types | |
| using | Ptr = shared_ptr< RandomizedRandomSampleConsensus< PointT > > |
| using | ConstPtr = shared_ptr< const RandomizedRandomSampleConsensus< 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 | |
| RandomizedRandomSampleConsensus (const SampleConsensusModelPtr &model) | |
| RRANSAC (Randomized RANdom SAmple Consensus) main constructor. More... |
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| RandomizedRandomSampleConsensus (const SampleConsensusModelPtr &model, double threshold) | |
| RRANSAC (Randomized RANdom 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::RandomizedRandomSampleConsensus< PointT >
RandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RANdom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O.
Chum and J. Matas, Proc. British Machine Vision Conf. (BMVC '02), vol. 2, BMVA, pp. 448-457, 2002.
The algorithm works similar to RANSAC, with one addition: after computing the model coefficients, randomly select a fraction of points. If any of these points do not belong to the model (given a threshold), continue with the next iteration instead of checking all points. This may speed up the finding of the model if the fraction of points to pre-test is chosen well.
- Note
- RRANSAC 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::RandomizedRandomSampleConsensus< PointT >::ConstPtr = shared_ptr<const RandomizedRandomSampleConsensus<PointT> > |
Ptr
template<typename PointT >
| using pcl::RandomizedRandomSampleConsensus< PointT >::Ptr = shared_ptr<RandomizedRandomSampleConsensus<PointT> > |
Constructor & Destructor Documentation
RandomizedRandomSampleConsensus() [1/2]
template<typename PointT >
| inline |
RRANSAC (Randomized RANdom SAmple Consensus) main constructor.
- Parameters
-
[in] model a Sample Consensus model
Definition at line 80 of file rransac.h.
References pcl::SampleConsensus< PointT >::max_iterations_.
RandomizedRandomSampleConsensus() [2/2]
template<typename PointT >
| inline |
RRANSAC (Randomized RANdom SAmple Consensus) main constructor.
- Parameters
-
[in] model a Sample Consensus model [in] threshold distance to model threshold
Definition at line 92 of file rransac.h.
References pcl::SampleConsensus< PointT >::max_iterations_.
Member Function Documentation
computeModel()
template<typename PointT >
| overridevirtual |
Compute the actual model and find the inliers.
- Parameters
-
[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 rransac.hpp.
References pcl_lrint.
getFractionNrPretest()
template<typename PointT >
| inline |
setFractionNrPretest()
template<typename PointT >
| inline |
The documentation for this class was generated from the following files:
- pcl/sample_consensus/rransac.h
- pcl/sample_consensus/impl/rransac.hpp
© 2009–2012, Willow Garage, Inc.
© 2012–, Open Perception, Inc.
Licensed under the BSD License.
https://pointclouds.org/documentation/classpcl_1_1_randomized_random_sample_consensus.html