Point Cloud Library (PCL)
1.9.1-dev
|
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>
Public Types | |
using | Ptr = boost::shared_ptr< MEstimatorSampleConsensus< PointT > > |
using | ConstPtr = boost::shared_ptr< const MEstimatorSampleConsensus< PointT > > |
![]() | |
using | Ptr = boost::shared_ptr< SampleConsensus< PointT > > |
using | ConstPtr = boost::shared_ptr< const SampleConsensus< PointT > > |
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... | |
![]() | |
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 IndicesPtr &indices, std::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 | |
![]() | |
double | rnd () |
Boost-based random number generator. More... | |
![]() | |
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... | |
std::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.
using pcl::MEstimatorSampleConsensus< PointT >::ConstPtr = boost::shared_ptr<const MEstimatorSampleConsensus<PointT> > |
using pcl::MEstimatorSampleConsensus< PointT >::Ptr = boost::shared_ptr<MEstimatorSampleConsensus<PointT> > |
|
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_.
|
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_.
|
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.
References pcl::geometry::distance().
Referenced by pcl::MEstimatorSampleConsensus< PointT >::MEstimatorSampleConsensus().