Point Cloud Library (PCL)
1.15.0-dev
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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 > > |
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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... | |
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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 ()=default |
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... | |
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double | rnd () |
Boost-based random number generator. More... | |
Additional Inherited Members | |
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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... | |
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.
using pcl::MaximumLikelihoodSampleConsensus< PointT >::ConstPtr = shared_ptr<const MaximumLikelihoodSampleConsensus<PointT> > |
using pcl::MaximumLikelihoodSampleConsensus< PointT >::Ptr = shared_ptr<MaximumLikelihoodSampleConsensus<PointT> > |
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MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor.
[in] | model | a Sample Consensus model |
Definition at line 78 of file mlesac.h.
References pcl::SampleConsensus< PointT >::max_iterations_.
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inline |
MLESAC (Maximum Likelihood Estimator SAmple Consensus) main constructor.
[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_.
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protected |
Compute the median value of a 3D point cloud using a given set point indices and return it as a Point32.
[in] | cloud | the point cloud data message |
[in] | indices | the point indices |
[out] | median | the resultant median value |
Definition at line 261 of file mlesac.hpp.
References pcl::computeMedian().
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Compute the median absolute deviation:
[in] | cloud | the point cloud data message |
[in] | indices | the set of point indices to use |
[in] | sigma | the sigma value |
Definition at line 212 of file mlesac.hpp.
References pcl::computeMedian().
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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 50 of file mlesac.hpp.
References pcl::geometry::distance(), pcl::getMinMax(), and M_PI.
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Determine the minimum and maximum 3D bounding box coordinates for a given set of points.
[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 237 of file mlesac.hpp.
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inline |