Point Cloud Library (PCL)
1.9.1-dev
|
SampleConsensus represents the base class. More...
#include <pcl/sample_consensus/sac.h>
Public Types | |
typedef boost::shared_ptr< SampleConsensus > | Ptr |
typedef boost::shared_ptr< const SampleConsensus > | ConstPtr |
Public Member Functions | |
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 | computeModel (int debug_verbosity_level=0)=0 |
Compute the actual model. 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... | |
Protected Member Functions | |
double | rnd () |
Boost-based random number generator. More... | |
Protected Attributes | |
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... | |
SampleConsensus represents the base class.
All sample consensus methods must inherit from this class.
typedef boost::shared_ptr<const SampleConsensus> pcl::SampleConsensus< T >::ConstPtr |
typedef boost::shared_ptr<SampleConsensus> pcl::SampleConsensus< T >::Ptr |
|
inline |
|
inline |
|
inlinevirtual |
|
pure virtual |
Compute the actual model.
Pure virtual.
Implemented in pcl::MaximumLikelihoodSampleConsensus< PointT >, pcl::RandomizedMEstimatorSampleConsensus< PointT >, pcl::RandomizedRandomSampleConsensus< PointT >, pcl::MEstimatorSampleConsensus< PointT >, pcl::LeastMedianSquares< PointT >, pcl::ProgressiveSampleConsensus< PointT >, and pcl::RandomSampleConsensus< PointT >.
Referenced by pcl::SampleConsensus< WeightSACPointType >::getProbability().
|
inline |
|
inline |
|
inline |
|
inline |
|
inline |
Return the model coefficients of the best model found so far.
[out] | model_coefficients | the resultant model coefficients, as documented in Module sample_consensus |
|
inline |
|
inline |
|
inline |
|
inlinevirtual |
Refine the model found.
This loops over the model coefficients and optimizes them together with the set of inliers, until the change in the set of inliers is minimal.
[in] | sigma | standard deviation multiplier for considering a sample as inlier (Mahalanobis distance) |
[in] | max_iterations | the maxim number of iterations to try to refine in case the inliers keep on changing |
|
inlineprotected |
Boost-based random number generator.
Definition at line 340 of file sac.h.
Referenced by pcl::SampleConsensus< WeightSACPointType >::getRandomSamples().
|
inline |
|
inline |
|
inline |
|
inline |
|
protected |
The indices of the points that were chosen as inliers after the last computeModel () call.
Definition at line 315 of file sac.h.
Referenced by pcl::SampleConsensus< WeightSACPointType >::getInliers(), and pcl::SampleConsensus< WeightSACPointType >::refineModel().
|
protected |
|
protected |
Maximum number of iterations before giving up.
Definition at line 330 of file sac.h.
Referenced by pcl::SampleConsensus< WeightSACPointType >::getMaxIterations(), and pcl::SampleConsensus< WeightSACPointType >::setMaxIterations().
|
protected |
The model found after the last computeModel () as point cloud indices.
Definition at line 312 of file sac.h.
Referenced by pcl::SampleConsensus< WeightSACPointType >::getModel().
|
protected |
The coefficients of our model computed directly from the model found.
Definition at line 318 of file sac.h.
Referenced by pcl::SampleConsensus< WeightSACPointType >::getModelCoefficients(), and pcl::SampleConsensus< WeightSACPointType >::refineModel().
|
protected |
Desired probability of choosing at least one sample free from outliers.
Definition at line 321 of file sac.h.
Referenced by pcl::SampleConsensus< WeightSACPointType >::getProbability(), and pcl::SampleConsensus< WeightSACPointType >::setProbability().
|
protected |
Boost-based random number generator distribution.
Definition at line 336 of file sac.h.
Referenced by pcl::SampleConsensus< WeightSACPointType >::SampleConsensus().
|
protected |
|
protected |
The underlying data model used (i.e.
what is it that we attempt to search for).
Definition at line 309 of file sac.h.
Referenced by pcl::SampleConsensus< WeightSACPointType >::getSampleConsensusModel(), pcl::SampleConsensus< WeightSACPointType >::refineModel(), and pcl::SampleConsensus< WeightSACPointType >::setSampleConsensusModel().
|
protected |
Distance to model threshold.
Definition at line 327 of file sac.h.
Referenced by pcl::SampleConsensus< WeightSACPointType >::getDistanceThreshold(), pcl::SampleConsensus< WeightSACPointType >::refineModel(), and pcl::SampleConsensus< WeightSACPointType >::setDistanceThreshold().