LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm. More...
#include <pcl/sample_consensus/lmeds.h>
Public Types | |
using | Ptr = shared_ptr< LeastMedianSquares< PointT > > |
using | ConstPtr = shared_ptr< const LeastMedianSquares< PointT > > |
Public Types inherited from pcl::SampleConsensus< PointT > | |
using | Ptr = shared_ptr< SampleConsensus< PointT > > |
using | ConstPtr = shared_ptr< const SampleConsensus< PointT > > |
Public Member Functions | |
LeastMedianSquares (const SampleConsensusModelPtr &model) | |
LMedS (Least Median of Squares) main constructor. More... |
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LeastMedianSquares (const SampleConsensusModelPtr &model, double threshold) | |
LMedS (Least Median of Squares) main constructor. More... |
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bool | computeModel (int debug_verbosity_level=0) override |
Compute the actual model and find the inliers. More... |
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Public Member Functions inherited from pcl::SampleConsensus< PointT > | |
SampleConsensus (const SampleConsensusModelPtr &model, bool random=false) | |
Constructor for base SAC. More... |
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SampleConsensus (const SampleConsensusModelPtr &model, double threshold, bool random=false) | |
Constructor for base SAC. More... |
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void | setSampleConsensusModel (const SampleConsensusModelPtr &model) |
Set the Sample Consensus model to use. More... |
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SampleConsensusModelPtr | getSampleConsensusModel () const |
Get the Sample Consensus model used. More... |
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virtual | ~SampleConsensus () |
Destructor for base SAC. More... |
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void | setDistanceThreshold (double threshold) |
Set the distance to model threshold. More... |
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double | getDistanceThreshold () const |
Get the distance to model threshold, as set by the user. More... |
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void | setMaxIterations (int max_iterations) |
Set the maximum number of iterations. More... |
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int | getMaxIterations () const |
Get the maximum number of iterations, as set by the user. More... |
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void | setProbability (double probability) |
Set the desired probability of choosing at least one sample free from outliers. More... |
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double | getProbability () const |
Obtain the probability of choosing at least one sample free from outliers, as set by the user. More... |
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void | setNumberOfThreads (const int nr_threads=-1) |
Set the number of threads to use or turn off parallelization. More... |
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int | getNumberOfThreads () const |
Get the number of threads, as set by the user. More... |
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virtual bool | refineModel (const double sigma=3.0, const unsigned int max_iterations=1000) |
Refine the model found. More... |
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void | getRandomSamples (const IndicesPtr &indices, std::size_t nr_samples, std::set< index_t > &indices_subset) |
Get a set of randomly selected indices. More... |
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void | getModel (Indices &model) const |
Return the best model found so far. More... |
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void | getInliers (Indices &inliers) const |
Return the best set of inliers found so far for this model. More... |
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void | getModelCoefficients (Eigen::VectorXf &model_coefficients) const |
Return the model coefficients of the best model found so far. More... |
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Additional Inherited Members | |
Protected Member Functions inherited from pcl::SampleConsensus< PointT > | |
double | rnd () |
Boost-based random number generator. More... |
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Protected Attributes inherited from pcl::SampleConsensus< PointT > | |
SampleConsensusModelPtr | sac_model_ |
The underlying data model used (i.e. More... |
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Indices | model_ |
The model found after the last computeModel () as point cloud indices. More... |
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Indices | inliers_ |
The indices of the points that were chosen as inliers after the last computeModel () call. More... |
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Eigen::VectorXf | model_coefficients_ |
The coefficients of our model computed directly from the model found. More... |
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double | probability_ |
Desired probability of choosing at least one sample free from outliers. More... |
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int | iterations_ |
Total number of internal loop iterations that we've done so far. More... |
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double | threshold_ |
Distance to model threshold. More... |
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int | max_iterations_ |
Maximum number of iterations before giving up. More... |
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int | threads_ |
The number of threads the scheduler should use, or a negative number if no parallelization is wanted. More... |
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boost::mt19937 | rng_alg_ |
Boost-based random number generator algorithm. More... |
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std::shared_ptr< boost::uniform_01< boost::mt19937 > > | rng_ |
Boost-based random number generator distribution. More... |
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LeastMedianSquares represents an implementation of the LMedS (Least Median of Squares) algorithm.
LMedS is a RANSAC-like model-fitting algorithm that can tolerate up to 50% outliers without requiring thresholds to be set. See Andrea Fusiello's "Elements of Geometric Computer Vision" (http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/FUSIELLO4/tutorial.html#x1-520007) for more details. In contrast to RANSAC, LMedS does not divide the points into inliers and outliers when finding the model. Instead, it uses the median of all point-model distances as the measure of how good a model is. A threshold is only needed at the end, when it is determined which points belong to the found model.
using pcl::LeastMedianSquares< PointT >::ConstPtr = shared_ptr<const LeastMedianSquares<PointT> > |
using pcl::LeastMedianSquares< PointT >::Ptr = shared_ptr<LeastMedianSquares<PointT> > |
| inline |
LMedS (Least Median of Squares) main constructor.
[in] | model | a Sample Consensus model |
Definition at line 78 of file lmeds.h.
References pcl::SampleConsensus< PointT >::max_iterations_.
| inline |
LMedS (Least Median of Squares) main constructor.
[in] | model | a Sample Consensus model |
[in] | threshold | distance to model threshold |
Definition at line 89 of file lmeds.h.
References 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 49 of file lmeds.hpp.
References pcl::computeMedian(), and pcl::geometry::distance().
© 2009–2012, Willow Garage, Inc.
© 2012–, Open Perception, Inc.
Licensed under the BSD License.
https://pointclouds.org/documentation/classpcl_1_1_least_median_squares.html