W3cubDocs
/PointCloudLibraryMaximumLikelihoodSampleConsensus 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.
Member Typedef Documentation
ConstPtr
template<typename PointT >
| using pcl::MaximumLikelihoodSampleConsensus< PointT >::ConstPtr = shared_ptr<const MaximumLikelihoodSampleConsensus<PointT> > |
Ptr
template<typename PointT >
| using pcl::MaximumLikelihoodSampleConsensus< PointT >::Ptr = shared_ptr<MaximumLikelihoodSampleConsensus<PointT> > |
Constructor & Destructor Documentation
MaximumLikelihoodSampleConsensus() [1/2]
template<typename PointT >
| 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 >
| 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 >
| 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 >
| protected |
Compute the median absolute deviation:
![\[ MAD = \sigma * median_i (| Xi - median_j(Xj) |) \]](data:image/png;base64,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)
- 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 >
| 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 >
| inline |
getMinMax()
template<typename PointT >
| 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 >
| inline |
The documentation for this class was generated from the following files:
- pcl/sample_consensus/mlesac.h
- pcl/sample_consensus/impl/mlesac.hpp
© 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