RandomSampleConsensus represents an implementation of the RANSAC (RANdom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Martin A. More...
#include <pcl/sample_consensus/ransac.h>
Inheritance diagram for pcl::RandomSampleConsensus< PointT >:
Collaboration diagram for pcl::RandomSampleConsensus< PointT >:
Public Types | |
| using | Ptr = shared_ptr< RandomSampleConsensus< PointT > > |
| using | ConstPtr = shared_ptr< const RandomSampleConsensus< PointT > > |
| Public Types inherited from pcl::SampleConsensus< PointT > | |
| using | Ptr = shared_ptr< SampleConsensus< PointT > > |
| using | ConstPtr = shared_ptr< const SampleConsensus< PointT > > |
Public Member Functions | |
| RandomSampleConsensus (const SampleConsensusModelPtr &model) | |
| RANSAC (RANdom SAmple Consensus) main constructor. More... | |
| RandomSampleConsensus (const SampleConsensusModelPtr &model, double threshold) | |
| RANSAC (RANdom 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 ()=default |
| 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... | |
| void | setNumberOfThreads (const int nr_threads=-1) |
| Set the number of threads to use or turn off parallelization. More... | |
| int | getNumberOfThreads () const |
| Get the number of threads, 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 IndicesPtr &indices, std::size_t nr_samples, std::set< index_t > &indices_subset) |
| Get a set of randomly selected indices. More... | |
| void | getModel (Indices &model) const |
| Return the best model found so far. More... | |
| void | getInliers (Indices &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... | |
| Indices | model_ |
| The model found after the last computeModel () as point cloud indices. More... | |
| Indices | 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... | |
| int | threads_ |
| The number of threads the scheduler should use, or a negative number if no parallelization is wanted. More... | |
| boost::mt19937 | rng_alg_ |
| Boost-based random number generator algorithm. More... | |
| std::shared_ptr< boost::uniform_01< boost::mt19937 > > | rng_ |
| Boost-based random number generator distribution. More... | |
Detailed Description
template<typename PointT>
class pcl::RandomSampleConsensus< PointT >
RandomSampleConsensus represents an implementation of the RANSAC (RANdom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Martin A.
Fischler and Robert C. Bolles, Comm. Of the ACM 24: 381–395, June 1981. A parallel variant is available, enable with setNumberOfThreads. Default is non-parallel.
The algorithm works as follows:
- randomly select samples from the cloud, just as many as needed to determine a model
- compute the coefficients of the model from the samples
- count how many points of the cloud belong to the model, given a threshold. These are called inliers
- repeat until a good model has been found or a max number of iterations has been reached
- return the model with the most inliers
Member Typedef Documentation
◆ ConstPtr
template<typename PointT >
| using pcl::RandomSampleConsensus< PointT >::ConstPtr = shared_ptr<const RandomSampleConsensus<PointT> > |
◆ Ptr
template<typename PointT >
| using pcl::RandomSampleConsensus< PointT >::Ptr = shared_ptr<RandomSampleConsensus<PointT> > |
Constructor & Destructor Documentation
◆ RandomSampleConsensus() [1/2]
template<typename PointT >
|
inline |
RANSAC (RANdom SAmple Consensus) main constructor.
- Parameters
-
[in] model a Sample Consensus model
Definition at line 86 of file ransac.h.
References pcl::SampleConsensus< PointT >::max_iterations_.
◆ RandomSampleConsensus() [2/2]
template<typename PointT >
|
inline |
RANSAC (RANdom SAmple Consensus) main constructor.
- Parameters
-
[in] model a Sample Consensus model [in] threshold distance to model threshold
Definition at line 97 of file ransac.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 57 of file ransac.hpp.
Referenced by pcl::registration::CorrespondenceRejectorSampleConsensus2D< PointT >::getRemainingCorrespondences(), and pcl::registration::CorrespondenceRejectorSampleConsensus< PointT >::getRemainingCorrespondences().
The documentation for this class was generated from the following files:
- pcl/sample_consensus/ransac.h
- pcl/sample_consensus/impl/ransac.hpp