SampleConsensus represents the base class. All sample consensus methods must inherit from this class. More...
#include <sac.h>
Public Types | |
| typedef boost::shared_ptr < const SampleConsensus > | ConstPtr |
| typedef boost::shared_ptr < SampleConsensus > | Ptr |
Public Member Functions | |
| virtual bool | computeModel (int debug_verbosity_level=0)=0 |
| Compute the actual model. Pure virtual. | |
| double | getDistanceThreshold () |
| Get the distance to model threshold, as set by the user. | |
| void | getInliers (std::vector< int > &inliers) |
| Return the best set of inliers found so far for this model. | |
| int | getMaxIterations () |
| Get the maximum number of iterations, as set by the user. | |
| void | getModel (std::vector< int > &model) |
| Return the best model found so far. | |
| void | getModelCoefficients (Eigen::VectorXf &model_coefficients) |
| Return the model coefficients of the best model found so far. | |
| double | getProbability () |
| Obtain the probability of choosing at least one sample free from outliers, as set by the user. | |
| void | getRandomSamples (const IndicesPtr &indices, size_t nr_samples, std::set< int > &indices_subset) |
| Get a set of randomly selected indices. | |
| SampleConsensus (const SampleConsensusModelPtr &model, double threshold) | |
| Constructor for base SAC. | |
| SampleConsensus (const SampleConsensusModelPtr &model) | |
| Constructor for base SAC. | |
| void | setDistanceThreshold (double threshold) |
| Set the distance to model threshold. | |
| void | setMaxIterations (int max_iterations) |
| Set the maximum number of iterations. | |
| void | setProbability (double probability) |
| Set the desired probability of choosing at least one sample free from outliers. | |
| virtual | ~SampleConsensus () |
| Destructor for base SAC. | |
Protected Attributes | |
| std::vector< int > | inliers_ |
| The indices of the points that were chosen as inliers after the last computeModel () call. | |
| int | iterations_ |
| Total number of internal loop iterations that we've done so far. | |
| int | max_iterations_ |
| Maximum number of iterations before giving up. | |
| std::vector< int > | model_ |
| The model found after the last computeModel () as point cloud indices. | |
| Eigen::VectorXf | model_coefficients_ |
| The coefficients of our model computed directly from the model found. | |
| double | probability_ |
| Desired probability of choosing at least one sample free from outliers. | |
| SampleConsensusModelPtr | sac_model_ |
| The underlying data model used (i.e. what is it that we attempt to search for). | |
| double | threshold_ |
| Distance to model threshold. | |
Private Types | |
| typedef SampleConsensusModel < T >::Ptr | SampleConsensusModelPtr |
Private Member Functions | |
| SampleConsensus () | |
| Constructor for base SAC. | |
SampleConsensus represents the base class. All sample consensus methods must inherit from this class.
Definition at line 53 of file sac.h.
| typedef boost::shared_ptr<const SampleConsensus> pcl::SampleConsensus< T >::ConstPtr |
| typedef boost::shared_ptr<SampleConsensus> pcl::SampleConsensus< T >::Ptr |
typedef SampleConsensusModel<T>::Ptr pcl::SampleConsensus< T >::SampleConsensusModelPtr [private] |
Reimplemented in pcl::LeastMedianSquares< PointT >, pcl::MaximumLikelihoodSampleConsensus< PointT >, pcl::MEstimatorSampleConsensus< PointT >, pcl::ProgressiveSampleConsensus< PointT >, pcl::RandomSampleConsensus< PointT >, pcl::RandomizedMEstimatorSampleConsensus< PointT >, and pcl::RandomizedRandomSampleConsensus< PointT >.
| pcl::SampleConsensus< T >::SampleConsensus | ( | ) | [inline, private] |
| pcl::SampleConsensus< T >::SampleConsensus | ( | const SampleConsensusModelPtr & | model | ) | [inline] |
| pcl::SampleConsensus< T >::SampleConsensus | ( | const SampleConsensusModelPtr & | model, | |
| double | threshold | |||
| ) | [inline] |
| virtual pcl::SampleConsensus< T >::~SampleConsensus | ( | ) | [inline, virtual] |
| virtual bool pcl::SampleConsensus< T >::computeModel | ( | int | debug_verbosity_level = 0 |
) | [pure virtual] |
Compute the actual model. Pure virtual.
Implemented in pcl::LeastMedianSquares< PointT >, pcl::MaximumLikelihoodSampleConsensus< PointT >, pcl::MEstimatorSampleConsensus< PointT >, pcl::ProgressiveSampleConsensus< PointT >, pcl::RandomSampleConsensus< PointT >, pcl::RandomizedMEstimatorSampleConsensus< PointT >, and pcl::RandomizedRandomSampleConsensus< PointT >.
| double pcl::SampleConsensus< T >::getDistanceThreshold | ( | ) | [inline] |
| void pcl::SampleConsensus< T >::getInliers | ( | std::vector< int > & | inliers | ) | [inline] |
| int pcl::SampleConsensus< T >::getMaxIterations | ( | ) | [inline] |
| void pcl::SampleConsensus< T >::getModel | ( | std::vector< int > & | model | ) | [inline] |
| void pcl::SampleConsensus< T >::getModelCoefficients | ( | Eigen::VectorXf & | model_coefficients | ) | [inline] |
| double pcl::SampleConsensus< T >::getProbability | ( | ) | [inline] |
| void pcl::SampleConsensus< T >::getRandomSamples | ( | const IndicesPtr & | indices, | |
| size_t | nr_samples, | |||
| std::set< int > & | indices_subset | |||
| ) | [inline] |
Get a set of randomly selected indices.
| indices | the input indices vector | |
| nr_samples | the desired number of point indices to randomly select | |
| indices_subset | the resultant output set of randomly selected indices |
Reimplemented in pcl::ProgressiveSampleConsensus< PointT >.
| void pcl::SampleConsensus< T >::setDistanceThreshold | ( | double | threshold | ) | [inline] |
| void pcl::SampleConsensus< T >::setMaxIterations | ( | int | max_iterations | ) | [inline] |
| void pcl::SampleConsensus< T >::setProbability | ( | double | probability | ) | [inline] |
std::vector<int> pcl::SampleConsensus< T >::inliers_ [protected] |
int pcl::SampleConsensus< T >::iterations_ [protected] |
int pcl::SampleConsensus< T >::max_iterations_ [protected] |
std::vector<int> pcl::SampleConsensus< T >::model_ [protected] |
Eigen::VectorXf pcl::SampleConsensus< T >::model_coefficients_ [protected] |
double pcl::SampleConsensus< T >::probability_ [protected] |
SampleConsensusModelPtr pcl::SampleConsensus< T >::sac_model_ [protected] |
double pcl::SampleConsensus< T >::threshold_ [protected] |
