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MaximumLikelihoodSampleConsensus represents an implementation of the MLESAC (Maximum Likelihood 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/mlesac.h>

Public Types

using Ptr = shared_ptr< MaximumLikelihoodSampleConsensus< PointT > >
using ConstPtr = shared_ptr< const MaximumLikelihoodSampleConsensus< PointT > >
- Public Types inherited from pcl::SampleConsensus< PointT >
using Ptr = shared_ptr< SampleConsensus< PointT > >
using ConstPtr = shared_ptr< const SampleConsensus< PointT > >

Public Member Functions

MaximumLikelihoodSampleConsensus (const SampleConsensusModelPtr &model)
MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor. More...
MaximumLikelihoodSampleConsensus (const SampleConsensusModelPtr &model, double threshold)
MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor. More...
bool computeModel (int debug_verbosity_level=0) override
Compute the actual model and find the inliers. More...
void setEMIterations (int iterations)
Set the number of EM iterations. More...
int getEMIterations () const
Get the number of EM iterations. 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 ()
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...

Protected Member Functions

double computeMedianAbsoluteDeviation (const PointCloudConstPtr &cloud, const IndicesPtr &indices, double sigma) const
Compute the median absolute deviation: More...
void getMinMax (const PointCloudConstPtr &cloud, const IndicesPtr &indices, Eigen::Vector4f &min_p, Eigen::Vector4f &max_p) const
Determine the minimum and maximum 3D bounding box coordinates for a given set of points. More...
void computeMedian (const PointCloudConstPtr &cloud, const IndicesPtr &indices, Eigen::Vector4f &median) const
Compute the median value of a 3D point cloud using a given set point indices and return it as a Point32. More...
- Protected Member Functions inherited from pcl::SampleConsensus< PointT >
double rnd ()
Boost-based random number generator. More...

Additional Inherited Members

- 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::MaximumLikelihoodSampleConsensus< PointT >

MaximumLikelihoodSampleConsensus represents an implementation of the MLESAC (Maximum Likelihood 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.

Note
MLESAC is useful in situations where most of the data samples belong to the model, and a fast outlier rejection algorithm is needed.
Author
Radu B. Rusu

Definition at line 57 of file mlesac.h.

Member Typedef Documentation

ConstPtr

template<typename PointT >
using pcl::MaximumLikelihoodSampleConsensus< PointT >::ConstPtr = shared_ptr<const MaximumLikelihoodSampleConsensus<PointT> >

Definition at line 64 of file mlesac.h.

Ptr

template<typename PointT >
using pcl::MaximumLikelihoodSampleConsensus< PointT >::Ptr = shared_ptr<MaximumLikelihoodSampleConsensus<PointT> >

Definition at line 63 of file mlesac.h.

Constructor & Destructor Documentation

MaximumLikelihoodSampleConsensus() [1/2]

template<typename PointT >
pcl::MaximumLikelihoodSampleConsensus< PointT >::MaximumLikelihoodSampleConsensus ( const SampleConsensusModelPtr & model )
inline

MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor.

Parameters
[in] model a Sample Consensus model

Definition at line 78 of file mlesac.h.

References pcl::SampleConsensus< PointT >::max_iterations_.

MaximumLikelihoodSampleConsensus() [2/2]

template<typename PointT >
pcl::MaximumLikelihoodSampleConsensus< PointT >::MaximumLikelihoodSampleConsensus ( const SampleConsensusModelPtr & model,
double threshold
)
inline

MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor.

Parameters
[in] model a Sample Consensus model
[in] threshold distance to model threshold

Definition at line 90 of file mlesac.h.

References pcl::SampleConsensus< PointT >::max_iterations_.

Member Function Documentation

computeMedian()

template<typename PointT >
void pcl::MaximumLikelihoodSampleConsensus< PointT >::computeMedian ( const PointCloudConstPtr & cloud,
const IndicesPtr & indices,
Eigen::Vector4f & median
) const
protected

Compute the median value of a 3D point cloud using a given set point indices and return it as a Point32.

Parameters
[in] cloud the point cloud data message
[in] indices the point indices
[out] median the resultant median value

Definition at line 258 of file mlesac.hpp.

References pcl::computeMedian().

computeMedianAbsoluteDeviation()

template<typename PointT >
double pcl::MaximumLikelihoodSampleConsensus< PointT >::computeMedianAbsoluteDeviation ( const PointCloudConstPtr & cloud,
const IndicesPtr & indices,
double sigma
) const
protected

Compute the median absolute deviation:

\[ MAD = \sigma * median_i (| Xi - median_j(Xj) |) \]

Note
Sigma needs to be chosen carefully (a good starting sigma value is 1.4826)
Parameters
[in] cloud the point cloud data message
[in] indices the set of point indices to use
[in] sigma the sigma value

Definition at line 209 of file mlesac.hpp.

References pcl::computeMedian().

computeModel()

template<typename PointT >
bool pcl::MaximumLikelihoodSampleConsensus< PointT >::computeModel ( int debug_verbosity_level = 0 )
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 50 of file mlesac.hpp.

References pcl::geometry::distance(), pcl::getMinMax(), and M_PI.

getEMIterations()

template<typename PointT >
int pcl::MaximumLikelihoodSampleConsensus< PointT >::getEMIterations ( ) const
inline

Get the number of EM iterations.

Definition at line 112 of file mlesac.h.

getMinMax()

template<typename PointT >
void pcl::MaximumLikelihoodSampleConsensus< PointT >::getMinMax ( const PointCloudConstPtr & cloud,
const IndicesPtr & indices,
Eigen::Vector4f & min_p,
Eigen::Vector4f & max_p
) const
protected

Determine the minimum and maximum 3D bounding box coordinates for a given set of points.

Parameters
[in] cloud the point cloud message
[in] indices the set of point indices to use
[out] min_p the resultant minimum bounding box coordinates
[out] max_p the resultant maximum bounding box coordinates

Definition at line 234 of file mlesac.hpp.

setEMIterations()

template<typename PointT >
void pcl::MaximumLikelihoodSampleConsensus< PointT >::setEMIterations ( int iterations )
inline

Set the number of EM iterations.

Parameters
[in] iterations the number of EM iterations

Definition at line 108 of file mlesac.h.


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_maximum_likelihood_sample_consensus.html