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| RandomizedRandomSampleConsensus (const SampleConsensusModelPtr &model) |
| RRANSAC (Randomized RANdom SAmple Consensus) main constructor. More...
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| RandomizedRandomSampleConsensus (const SampleConsensusModelPtr &model, double threshold) |
| RRANSAC (Randomized RANdom 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::RandomizedRandomSampleConsensus< PointT >
RandomizedRandomSampleConsensus represents an implementation of the RRANSAC (Randomized RANdom SAmple Consensus), as described in "Randomized RANSAC with Td,d test", O.
Chum and J. Matas, Proc. British Machine Vision Conf. (BMVC '02), vol. 2, BMVA, pp. 448-457, 2002.
The algorithm works similar to RANSAC, with one addition: after computing the model coefficients, randomly select a fraction of points. If any of these points do not belong to the model (given a threshold), continue with the next iteration instead of checking all points. This may speed up the finding of the model if the fraction of points to pre-test is chosen well.
- Note
- RRANSAC 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 60 of file rransac.h.