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| RandomizedMEstimatorSampleConsensus (const SampleConsensusModelPtr &model) |
| RMSAC (Randomized M-estimator SAmple Consensus) main constructor. More...
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| RandomizedMEstimatorSampleConsensus (const SampleConsensusModelPtr &model, double threshold) |
| RMSAC (Randomized M-estimator SAmple Consensus) 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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void | setFractionNrPretest (double nr_pretest) |
| Set the percentage of points to pre-test. More...
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double | getFractionNrPretest () const |
| Get the percentage of points to pre-test. More...
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| 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 ()=default |
| 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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double | rnd () |
| Boost-based random number generator. More...
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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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template<typename PointT>
class pcl::RandomizedMEstimatorSampleConsensus< PointT >
RandomizedMEstimatorSampleConsensus represents an implementation of the RMSAC (Randomized M-estimator SAmple Consensus) algorithm, which basically adds a Td,d test (see RandomizedRandomSampleConsensus) to an MSAC estimator (see MEstimatorSampleConsensus).
- Note
- RMSAC is useful in situations where most of the data samples belong to the model, and a fast outlier rejection algorithm is needed.
- Author
- Radu B. Rusu
Definition at line 56 of file rmsac.h.