Point Cloud Library (PCL)
1.9.1
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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>
Public Types | |
typedef boost::shared_ptr< RandomSampleConsensus > | Ptr |
typedef boost::shared_ptr< const RandomSampleConsensus > | ConstPtr |
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typedef boost::shared_ptr< SampleConsensus > | Ptr |
typedef boost::shared_ptr< const SampleConsensus > | ConstPtr |
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) |
Compute the actual model and find the inliers. More... | |
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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 () |
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 () |
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 () |
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) |
Return the best model found so far. More... | |
void | getInliers (std::vector< int > &inliers) |
Return the best set of inliers found so far for this model. More... | |
void | getModelCoefficients (Eigen::VectorXf &model_coefficients) |
Return the model coefficients of the best model found so far. More... | |
Additional Inherited Members | |
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double | rnd () |
Boost-based random number generator. More... | |
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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... | |
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.
typedef boost::shared_ptr<const RandomSampleConsensus> pcl::RandomSampleConsensus< PointT >::ConstPtr |
typedef boost::shared_ptr<RandomSampleConsensus> pcl::RandomSampleConsensus< PointT >::Ptr |
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inline |
RANSAC (RAndom SAmple Consensus) main constructor.
[in] | model | a Sample Consensus model |
Definition at line 76 of file ransac.h.
References pcl::SampleConsensus< PointT >::max_iterations_.
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inline |
RANSAC (RAndom SAmple Consensus) main constructor.
[in] | model | a Sample Consensus model |
[in] | threshold | distance to model threshold |
Definition at line 87 of file ransac.h.
References pcl::RandomSampleConsensus< PointT >::computeModel(), and pcl::SampleConsensus< PointT >::max_iterations_.
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virtual |
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 ransac.hpp.
Referenced by pcl::registration::CorrespondenceRejectorSampleConsensus2D< PointT >::getRemainingCorrespondences(), pcl::registration::CorrespondenceRejectorSampleConsensus< PointT >::getRemainingCorrespondences(), and pcl::RandomSampleConsensus< PointT >::RandomSampleConsensus().