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>
Inheritance diagram for pcl::MEstimatorSampleConsensus< PointT >:
Public Types | |
| typedef boost::shared_ptr < MEstimatorSampleConsensus > | Ptr |
| typedef boost::shared_ptr < const MEstimatorSampleConsensus > | ConstPtr |
| Public Types inherited from pcl::SampleConsensus< PointT > | |
| typedef boost::shared_ptr < SampleConsensus > | Ptr |
| typedef boost::shared_ptr < const SampleConsensus > | ConstPtr |
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) |
| Compute the actual model and find the inliers. 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 () |
| 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 () |
| 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 () |
| 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 boost::shared_ptr< std::vector< int > > &indices, size_t nr_samples, std::set< int > &indices_subset) |
| Get a set of randomly selected indices. More... | |
| void | getModel (std::vector< int > &model) |
| Return the best model found so far. More... | |
| void | getInliers (std::vector< int > &inliers) |
| Return the best set of inliers found so far for this model. More... | |
| void | getModelCoefficients (Eigen::VectorXf &model_coefficients) |
| Return the model coefficients of the best model found so far. More... | |
Additional Inherited Members | |
| Protected Member Functions inherited from pcl::SampleConsensus< PointT > | |
| double | rnd () |
| Boost-based random number generator. More... | |
| Protected Attributes inherited from pcl::SampleConsensus< PointT > | |
| 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... | |
| boost::shared_ptr < boost::uniform_01 < boost::mt19937 > > | rng_ |
| Boost-based random number generator distribution. More... | |
Detailed Description
template<typename PointT>
class pcl::MEstimatorSampleConsensus< PointT >
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.
Member Typedef Documentation
template<typename PointT >
| typedef boost::shared_ptr<const MEstimatorSampleConsensus> pcl::MEstimatorSampleConsensus< PointT >::ConstPtr |
template<typename PointT >
| typedef boost::shared_ptr<MEstimatorSampleConsensus> pcl::MEstimatorSampleConsensus< PointT >::Ptr |
Constructor & Destructor Documentation
template<typename PointT >
|
inline |
MSAC (M-estimator SAmple Consensus) main constructor.
- Parameters
-
[in] model a Sample Consensus model
Definition at line 77 of file msac.h.
References pcl::SampleConsensus< PointT >::max_iterations_.
template<typename PointT >
|
inline |
MSAC (M-estimator SAmple Consensus) main constructor.
- Parameters
-
[in] model a Sample Consensus model [in] threshold distance to model threshold
Definition at line 88 of file msac.h.
References pcl::SampleConsensus< PointT >::max_iterations_.
Member Function Documentation
template<typename PointT >
|
virtual |
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 >.
The documentation for this class was generated from the following files: