Pose estimation and alignment class using a prerejective RANSAC routine. More...
#include <pcl/registration/sample_consensus_prerejective.h>
Public Member Functions |
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| SampleConsensusPrerejective () | |
| Constructor. More... |
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| ~SampleConsensusPrerejective () | |
| Destructor. More... |
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| void | setSourceFeatures (const FeatureCloudConstPtr &features) |
| Provide a boost shared pointer to the source point cloud's feature descriptors. More... |
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| const FeatureCloudConstPtr | getSourceFeatures () const |
| Get a pointer to the source point cloud's features. More... |
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| void | setTargetFeatures (const FeatureCloudConstPtr &features) |
| Provide a boost shared pointer to the target point cloud's feature descriptors. More... |
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| const FeatureCloudConstPtr | getTargetFeatures () const |
| Get a pointer to the target point cloud's features. More... |
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| void | setNumberOfSamples (int nr_samples) |
| Set the number of samples to use during each iteration. More... |
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| int | getNumberOfSamples () const |
| Get the number of samples to use during each iteration, as set by the user. More... |
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| void | setCorrespondenceRandomness (int k) |
| Set the number of neighbors to use when selecting a random feature correspondence. More... |
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| int | getCorrespondenceRandomness () const |
| Get the number of neighbors used when selecting a random feature correspondence, as set by the user. More... |
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| void | setSimilarityThreshold (float similarity_threshold) |
| Set the similarity threshold in [0,1[ between edge lengths of the underlying polygonal correspondence rejector object, where 1 is a perfect match. More... |
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| float | getSimilarityThreshold () const |
| Get the similarity threshold between edge lengths of the underlying polygonal correspondence rejector object,. More... |
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| void | setInlierFraction (float inlier_fraction) |
| Set the required inlier fraction (of the input) More... |
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| float | getInlierFraction () const |
| Get the required inlier fraction. More... |
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| const pcl::Indices & | getInliers () const |
| Get the inlier indices of the source point cloud under the final transformation. More... |
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 Public Member Functions inherited from pcl::Registration< PointSource, PointTarget > |
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| Registration () | |
| Empty constructor. More... |
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| ~Registration () | |
| destructor. More... |
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| void | setTransformationEstimation (const TransformationEstimationPtr &te) |
| Provide a pointer to the transformation estimation object. More... |
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| void | setCorrespondenceEstimation (const CorrespondenceEstimationPtr &ce) |
| Provide a pointer to the correspondence estimation object. More... |
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| virtual void | setInputSource (const PointCloudSourceConstPtr &cloud) |
| Provide a pointer to the input source (e.g., the point cloud that we want to align to the target) More... |
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| const PointCloudSourceConstPtr | getInputSource () |
| Get a pointer to the input point cloud dataset target. More... |
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| virtual void | setInputTarget (const PointCloudTargetConstPtr &cloud) |
| Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to) More... |
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| const PointCloudTargetConstPtr | getInputTarget () |
| Get a pointer to the input point cloud dataset target. More... |
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| void | setSearchMethodTarget (const KdTreePtr &tree, bool force_no_recompute=false) |
| Provide a pointer to the search object used to find correspondences in the target cloud. More... |
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| KdTreePtr | getSearchMethodTarget () const |
| Get a pointer to the search method used to find correspondences in the target cloud. More... |
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| void | setSearchMethodSource (const KdTreeReciprocalPtr &tree, bool force_no_recompute=false) |
| Provide a pointer to the search object used to find correspondences in the source cloud (usually used by reciprocal correspondence finding). More... |
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| KdTreeReciprocalPtr | getSearchMethodSource () const |
| Get a pointer to the search method used to find correspondences in the source cloud. More... |
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| Matrix4 | getFinalTransformation () |
| Get the final transformation matrix estimated by the registration method. More... |
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| Matrix4 | getLastIncrementalTransformation () |
| Get the last incremental transformation matrix estimated by the registration method. More... |
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| void | setMaximumIterations (int nr_iterations) |
| Set the maximum number of iterations the internal optimization should run for. More... |
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| int | getMaximumIterations () |
| Get the maximum number of iterations the internal optimization should run for, as set by the user. More... |
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| void | setRANSACIterations (int ransac_iterations) |
| Set the number of iterations RANSAC should run for. More... |
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| double | getRANSACIterations () |
| Get the number of iterations RANSAC should run for, as set by the user. More... |
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| void | setRANSACOutlierRejectionThreshold (double inlier_threshold) |
| Set the inlier distance threshold for the internal RANSAC outlier rejection loop. More... |
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| double | getRANSACOutlierRejectionThreshold () |
| Get the inlier distance threshold for the internal outlier rejection loop as set by the user. More... |
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| void | setMaxCorrespondenceDistance (double distance_threshold) |
| Set the maximum distance threshold between two correspondent points in source <-> target. More... |
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| double | getMaxCorrespondenceDistance () |
| Get the maximum distance threshold between two correspondent points in source <-> target. More... |
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| void | setTransformationEpsilon (double epsilon) |
| Set the transformation epsilon (maximum allowable translation squared difference between two consecutive transformations) in order for an optimization to be considered as having converged to the final solution. More... |
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| double | getTransformationEpsilon () |
| Get the transformation epsilon (maximum allowable translation squared difference between two consecutive transformations) as set by the user. More... |
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| void | setTransformationRotationEpsilon (double epsilon) |
| Set the transformation rotation epsilon (maximum allowable rotation difference between two consecutive transformations) in order for an optimization to be considered as having converged to the final solution. More... |
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| double | getTransformationRotationEpsilon () |
| Get the transformation rotation epsilon (maximum allowable difference between two consecutive transformations) as set by the user (epsilon is the cos(angle) in a axis-angle representation). More... |
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| void | setEuclideanFitnessEpsilon (double epsilon) |
| Set the maximum allowed Euclidean error between two consecutive steps in the ICP loop, before the algorithm is considered to have converged. More... |
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| double | getEuclideanFitnessEpsilon () |
| Get the maximum allowed distance error before the algorithm will be considered to have converged, as set by the user. More... |
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| void | setPointRepresentation (const PointRepresentationConstPtr &point_representation) |
| Provide a boost shared pointer to the PointRepresentation to be used when comparing points. More... |
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| bool | registerVisualizationCallback (std::function< UpdateVisualizerCallbackSignature > &visualizerCallback) |
| Register the user callback function which will be called from registration thread in order to update point cloud obtained after each iteration. More... |
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| double | getFitnessScore (double max_range=std::numeric_limits< double >::max()) |
| Obtain the Euclidean fitness score (e.g., mean of squared distances from the source to the target) More... |
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| double | getFitnessScore (const std::vector< float > &distances_a, const std::vector< float > &distances_b) |
| Obtain the Euclidean fitness score (e.g., mean of squared distances from the source to the target) from two sets of correspondence distances (distances between source and target points) More... |
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| bool | hasConverged () const |
| Return the state of convergence after the last align run. More... |
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| void | align (PointCloudSource &output) |
| Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output. More... |
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| void | align (PointCloudSource &output, const Matrix4 &guess) |
| Call the registration algorithm which estimates the transformation and returns the transformed source (input) as output. More... |
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| const std::string & | getClassName () const |
| Abstract class get name method. More... |
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| bool | initCompute () |
| Internal computation initialization. More... |
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| bool | initComputeReciprocal () |
| Internal computation when reciprocal lookup is needed. More... |
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| void | addCorrespondenceRejector (const CorrespondenceRejectorPtr &rejector) |
| Add a new correspondence rejector to the list. More... |
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| std::vector< CorrespondenceRejectorPtr > | getCorrespondenceRejectors () |
| Get the list of correspondence rejectors. More... |
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| bool | removeCorrespondenceRejector (unsigned int i) |
| Remove the i-th correspondence rejector in the list. More... |
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| void | clearCorrespondenceRejectors () |
| Clear the list of correspondence rejectors. More... |
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| Public Member Functions inherited from pcl::PCLBase< PointSource > |
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| PCLBase () | |
| Empty constructor. More... |
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| PCLBase (const PCLBase &base) | |
| Copy constructor. More... |
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| virtual | ~PCLBase ()=default |
| Destructor. More... |
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| virtual void | setInputCloud (const PointCloudConstPtr &cloud) |
| Provide a pointer to the input dataset. More... |
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| const PointCloudConstPtr | getInputCloud () const |
| Get a pointer to the input point cloud dataset. More... |
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| virtual void | setIndices (const IndicesPtr &indices) |
| Provide a pointer to the vector of indices that represents the input data. More... |
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| virtual void | setIndices (const IndicesConstPtr &indices) |
| Provide a pointer to the vector of indices that represents the input data. More... |
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| virtual void | setIndices (const PointIndicesConstPtr &indices) |
| Provide a pointer to the vector of indices that represents the input data. More... |
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| virtual void | setIndices (std::size_t row_start, std::size_t col_start, std::size_t nb_rows, std::size_t nb_cols) |
| Set the indices for the points laying within an interest region of the point cloud. More... |
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| IndicesPtr | getIndices () |
| Get a pointer to the vector of indices used. More... |
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| const IndicesConstPtr | getIndices () const |
| Get a pointer to the vector of indices used. More... |
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| const PointSource & | operator[] (std::size_t pos) const |
| Override PointCloud operator[] to shorten code. More... |
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Protected Member Functions |
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| int | getRandomIndex (int n) const |
| Choose a random index between 0 and n-1. More... |
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| void | selectSamples (const PointCloudSource &cloud, int nr_samples, pcl::Indices &sample_indices) |
| Select nr_samples sample points from cloud while making sure that their pairwise distances are greater than a user-defined minimum distance, min_sample_distance. More... |
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| void | findSimilarFeatures (const pcl::Indices &sample_indices, std::vector< pcl::Indices > &similar_features, pcl::Indices &corresponding_indices) |
| For each of the sample points, find a list of points in the target cloud whose features are similar to the sample points' features. More... |
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| void | computeTransformation (PointCloudSource &output, const Eigen::Matrix4f &guess) override |
| Rigid transformation computation method. More... |
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| void | getFitness (pcl::Indices &inliers, float &fitness_score) |
| Obtain the fitness of a transformation The following metrics are calculated, based on final_transformation_ and corr_dist_threshold_: More... |
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| Protected Member Functions inherited from pcl::Registration< PointSource, PointTarget > |
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| bool | searchForNeighbors (const PointCloudSource &cloud, int index, pcl::Indices &indices, std::vector< float > &distances) |
| Search for the closest nearest neighbor of a given point. More... |
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| virtual void | computeTransformation (PointCloudSource &output, const Matrix4 &guess)=0 |
| Abstract transformation computation method with initial guess. More... |
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| Protected Member Functions inherited from pcl::PCLBase< PointSource > |
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| bool | initCompute () |
| This method should get called before starting the actual computation. More... |
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| bool | deinitCompute () |
| This method should get called after finishing the actual computation. More... |
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Protected Attributes |
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| FeatureCloudConstPtr | input_features_ |
| The source point cloud's feature descriptors. More... |
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| FeatureCloudConstPtr | target_features_ |
| The target point cloud's feature descriptors. More... |
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| int | nr_samples_ |
| The number of samples to use during each iteration. More... |
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| int | k_correspondences_ |
| The number of neighbors to use when selecting a random feature correspondence. More... |
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| FeatureKdTreePtr | feature_tree_ |
| The KdTree used to compare feature descriptors. More... |
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| CorrespondenceRejectorPolyPtr | correspondence_rejector_poly_ |
| The polygonal correspondence rejector used for prerejection. More... |
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| float | inlier_fraction_ |
| The fraction [0,1] of inlier points required for accepting a transformation. More... |
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| pcl::Indices | inliers_ |
| Inlier points of final transformation as indices into source. More... |
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| Protected Attributes inherited from pcl::Registration< PointSource, PointTarget > |
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| std::string | reg_name_ |
| The registration method name. More... |
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| KdTreePtr | tree_ |
| A pointer to the spatial search object. More... |
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| KdTreeReciprocalPtr | tree_reciprocal_ |
| A pointer to the spatial search object of the source. More... |
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| int | nr_iterations_ |
| The number of iterations the internal optimization ran for (used internally). More... |
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| int | max_iterations_ |
| The maximum number of iterations the internal optimization should run for. More... |
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| int | ransac_iterations_ |
| The number of iterations RANSAC should run for. More... |
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| PointCloudTargetConstPtr | target_ |
| The input point cloud dataset target. More... |
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| Matrix4 | final_transformation_ |
| The final transformation matrix estimated by the registration method after N iterations. More... |
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| Matrix4 | transformation_ |
| The transformation matrix estimated by the registration method. More... |
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| Matrix4 | previous_transformation_ |
| The previous transformation matrix estimated by the registration method (used internally). More... |
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| double | transformation_epsilon_ |
| The maximum difference between two consecutive transformations in order to consider convergence (user defined). More... |
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| double | transformation_rotation_epsilon_ |
| The maximum rotation difference between two consecutive transformations in order to consider convergence (user defined). More... |
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| double | euclidean_fitness_epsilon_ |
| The maximum allowed Euclidean error between two consecutive steps in the ICP loop, before the algorithm is considered to have converged. More... |
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| double | corr_dist_threshold_ |
| The maximum distance threshold between two correspondent points in source <-> target. More... |
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| double | inlier_threshold_ |
| The inlier distance threshold for the internal RANSAC outlier rejection loop. More... |
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| bool | converged_ |
| Holds internal convergence state, given user parameters. More... |
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| int | min_number_correspondences_ |
| The minimum number of correspondences that the algorithm needs before attempting to estimate the transformation. More... |
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| CorrespondencesPtr | correspondences_ |
| The set of correspondences determined at this ICP step. More... |
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| TransformationEstimationPtr | transformation_estimation_ |
| A TransformationEstimation object, used to calculate the 4x4 rigid transformation. More... |
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| CorrespondenceEstimationPtr | correspondence_estimation_ |
| A CorrespondenceEstimation object, used to estimate correspondences between the source and the target cloud. More... |
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| std::vector< CorrespondenceRejectorPtr > | correspondence_rejectors_ |
| The list of correspondence rejectors to use. More... |
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| bool | target_cloud_updated_ |
| Variable that stores whether we have a new target cloud, meaning we need to pre-process it again. More... |
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| bool | source_cloud_updated_ |
| Variable that stores whether we have a new source cloud, meaning we need to pre-process it again. More... |
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| bool | force_no_recompute_ |
| A flag which, if set, means the tree operating on the target cloud will never be recomputed. More... |
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| bool | force_no_recompute_reciprocal_ |
| A flag which, if set, means the tree operating on the source cloud will never be recomputed. More... |
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| std::function< UpdateVisualizerCallbackSignature > | update_visualizer_ |
| Callback function to update intermediate source point cloud position during it's registration to the target point cloud. More... |
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| Protected Attributes inherited from pcl::PCLBase< PointSource > |
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| PointCloudConstPtr | input_ |
| The input point cloud dataset. More... |
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| IndicesPtr | indices_ |
| A pointer to the vector of point indices to use. More... |
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| bool | use_indices_ |
| Set to true if point indices are used. More... |
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| bool | fake_indices_ |
| If no set of indices are given, we construct a set of fake indices that mimic the input PointCloud. More... |
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Detailed Description
template<typename PointSource, typename PointTarget, typename FeatureT>
class pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >
Pose estimation and alignment class using a prerejective RANSAC routine.
This class inserts a simple, yet effective "prerejection" step into the standard RANSAC pose estimation loop in order to avoid verification of pose hypotheses that are likely to be wrong. This is achieved by local pose-invariant geometric constraints, as also implemented in the class CorrespondenceRejectorPoly.
In order to robustly align partial/occluded models, this routine performs fit error evaluation using only inliers, i.e. points closer than a Euclidean threshold, which is specifiable using setInlierFraction().
The amount of prerejection or "greedyness" of the algorithm can be specified using setSimilarityThreshold() in [0,1[, where a value of 0 means disabled, and 1 is maximally rejective.
If you use this in academic work, please cite:
A. G. Buch, D. Kraft, J.-K. Kämäräinen, H. G. Petersen and N. Krüger. Pose Estimation using Local Structure-Specific Shape and Appearance Context. International Conference on Robotics and Automation (ICRA), 2013.
Definition at line 75 of file sample_consensus_prerejective.h.
Member Typedef Documentation
ConstPtr
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::ConstPtr = shared_ptr<const SampleConsensusPrerejective<PointSource, PointTarget, FeatureT> > |
Definition at line 110 of file sample_consensus_prerejective.h.
CorrespondenceRejectorPoly
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::CorrespondenceRejectorPoly = pcl::registration::CorrespondenceRejectorPoly<PointSource, PointTarget> |
Definition at line 115 of file sample_consensus_prerejective.h.
CorrespondenceRejectorPolyConstPtr
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::CorrespondenceRejectorPolyConstPtr = typename CorrespondenceRejectorPoly::ConstPtr |
Definition at line 118 of file sample_consensus_prerejective.h.
CorrespondenceRejectorPolyPtr
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::CorrespondenceRejectorPolyPtr = typename CorrespondenceRejectorPoly::Ptr |
Definition at line 116 of file sample_consensus_prerejective.h.
FeatureCloud
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::FeatureCloud = pcl::PointCloud<FeatureT> |
Definition at line 103 of file sample_consensus_prerejective.h.
FeatureCloudConstPtr
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::FeatureCloudConstPtr = typename FeatureCloud::ConstPtr |
Definition at line 105 of file sample_consensus_prerejective.h.
FeatureCloudPtr
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::FeatureCloudPtr = typename FeatureCloud::Ptr |
Definition at line 104 of file sample_consensus_prerejective.h.
FeatureKdTreePtr
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::FeatureKdTreePtr = typename KdTreeFLANN<FeatureT>::Ptr |
Definition at line 112 of file sample_consensus_prerejective.h.
Matrix4
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::Matrix4 = typename Registration<PointSource, PointTarget>::Matrix4 |
Definition at line 77 of file sample_consensus_prerejective.h.
PointCloudSource
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::PointCloudSource = typename Registration<PointSource, PointTarget>::PointCloudSource |
Definition at line 93 of file sample_consensus_prerejective.h.
PointCloudSourceConstPtr
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::PointCloudSourceConstPtr = typename PointCloudSource::ConstPtr |
Definition at line 95 of file sample_consensus_prerejective.h.
PointCloudSourcePtr
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::PointCloudSourcePtr = typename PointCloudSource::Ptr |
Definition at line 94 of file sample_consensus_prerejective.h.
PointCloudTarget
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::PointCloudTarget = typename Registration<PointSource, PointTarget>::PointCloudTarget |
Definition at line 98 of file sample_consensus_prerejective.h.
PointIndicesConstPtr
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::PointIndicesConstPtr = PointIndices::ConstPtr |
Definition at line 101 of file sample_consensus_prerejective.h.
PointIndicesPtr
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::PointIndicesPtr = PointIndices::Ptr |
Definition at line 100 of file sample_consensus_prerejective.h.
Ptr
| using pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::Ptr = shared_ptr<SampleConsensusPrerejective<PointSource, PointTarget, FeatureT> > |
Definition at line 108 of file sample_consensus_prerejective.h.
Constructor & Destructor Documentation
SampleConsensusPrerejective()
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inline |
Constructor.
Definition at line 121 of file sample_consensus_prerejective.h.
References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::correspondence_rejector_poly_, pcl::Registration< PointSource, PointTarget >::max_iterations_, pcl::Registration< PointSource, PointTarget >::reg_name_, and pcl::Registration< PointSource, PointTarget >::transformation_estimation_.
~SampleConsensusPrerejective()
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inline |
Destructor.
Definition at line 138 of file sample_consensus_prerejective.h.
Member Function Documentation
computeTransformation()
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overrideprotected |
Rigid transformation computation method.
- Parameters
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output the transformed input point cloud dataset using the rigid transformation found guess The computed transformation
Definition at line 154 of file sample_consensus_prerejective.hpp.
References pcl::transformPointCloud().
findSimilarFeatures()
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protected |
For each of the sample points, find a list of points in the target cloud whose features are similar to the sample points' features.
From these, select one randomly which will be considered that sample point's correspondence.
- Parameters
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sample_indices the indices of each sample point similar_features correspondence cache, which is used to read/write already computed correspondences corresponding_indices the resulting indices of each sample's corresponding point in the target cloud
Definition at line 120 of file sample_consensus_prerejective.hpp.
getCorrespondenceRandomness()
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inline |
Get the number of neighbors used when selecting a random feature correspondence, as set by the user.
Definition at line 197 of file sample_consensus_prerejective.h.
References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::k_correspondences_.
getFitness()
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protected |
Obtain the fitness of a transformation The following metrics are calculated, based on final_transformation_ and corr_dist_threshold_:
- Inliers: the number of transformed points which are closer than threshold to NN
- Error score: the MSE of the inliers
- Parameters
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inliers indices of source point cloud inliers fitness_score output fitness score as RMSE
Definition at line 307 of file sample_consensus_prerejective.hpp.
References pcl::transformPointCloud().
getInlierFraction()
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inline |
Get the required inlier fraction.
- Returns
- required inlier fraction in [0,1]
Definition at line 234 of file sample_consensus_prerejective.h.
References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::inlier_fraction_.
getInliers()
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inline |
Get the inlier indices of the source point cloud under the final transformation.
- Returns
- inlier indices
Definition at line 244 of file sample_consensus_prerejective.h.
References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::inliers_.
getNumberOfSamples()
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inline |
Get the number of samples to use during each iteration, as set by the user.
Definition at line 178 of file sample_consensus_prerejective.h.
References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::nr_samples_.
getRandomIndex()
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inlineprotected |
Choose a random index between 0 and n-1.
- Parameters
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n the number of possible indices to choose from
Definition at line 254 of file sample_consensus_prerejective.h.
getSimilarityThreshold()
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inline |
Get the similarity threshold between edge lengths of the underlying polygonal correspondence rejector object,.
- Returns
- edge length similarity threshold
Definition at line 216 of file sample_consensus_prerejective.h.
References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::correspondence_rejector_poly_.
getSourceFeatures()
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inline |
Get a pointer to the source point cloud's features.
Definition at line 148 of file sample_consensus_prerejective.h.
References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::input_features_.
getTargetFeatures()
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inline |
Get a pointer to the target point cloud's features.
Definition at line 161 of file sample_consensus_prerejective.h.
References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::target_features_.
selectSamples()
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protected |
Select nr_samples sample points from cloud while making sure that their pairwise distances are greater than a user-defined minimum distance, min_sample_distance.
- Parameters
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cloud the input point cloud nr_samples the number of samples to select sample_indices the resulting sample indices
Definition at line 77 of file sample_consensus_prerejective.hpp.
setCorrespondenceRandomness()
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inline |
Set the number of neighbors to use when selecting a random feature correspondence.
A higher value will add more randomness to the feature matching.
- Parameters
-
k the number of neighbors to use when selecting a random feature correspondence.
Definition at line 189 of file sample_consensus_prerejective.h.
References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::k_correspondences_.
setInlierFraction()
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inline |
Set the required inlier fraction (of the input)
- Parameters
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inlier_fraction required inlier fraction, must be in [0,1]
Definition at line 225 of file sample_consensus_prerejective.h.
References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::inlier_fraction_.
setNumberOfSamples()
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inline |
Set the number of samples to use during each iteration.
- Parameters
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nr_samples the number of samples to use during each iteration
Definition at line 170 of file sample_consensus_prerejective.h.
References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::nr_samples_.
setSimilarityThreshold()
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inline |
Set the similarity threshold in [0,1[ between edge lengths of the underlying polygonal correspondence rejector object, where 1 is a perfect match.
- Parameters
-
similarity_threshold edge length similarity threshold
Definition at line 207 of file sample_consensus_prerejective.h.
References pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::correspondence_rejector_poly_.
setSourceFeatures()
| void pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::setSourceFeatures | ( | const FeatureCloudConstPtr & | features | ) |
Provide a boost shared pointer to the source point cloud's feature descriptors.
- Parameters
-
features the source point cloud's features
Definition at line 48 of file sample_consensus_prerejective.hpp.
setTargetFeatures()
| void pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::setTargetFeatures | ( | const FeatureCloudConstPtr & | features | ) |
Provide a boost shared pointer to the target point cloud's feature descriptors.
- Parameters
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features the target point cloud's features
Definition at line 62 of file sample_consensus_prerejective.hpp.
Member Data Documentation
correspondence_rejector_poly_
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protected |
The polygonal correspondence rejector used for prerejection.
Definition at line 318 of file sample_consensus_prerejective.h.
Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getSimilarityThreshold(), pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::SampleConsensusPrerejective(), and pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::setSimilarityThreshold().
feature_tree_
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protected |
The KdTree used to compare feature descriptors.
Definition at line 315 of file sample_consensus_prerejective.h.
inlier_fraction_
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protected |
The fraction [0,1] of inlier points required for accepting a transformation.
Definition at line 322 of file sample_consensus_prerejective.h.
Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getInlierFraction(), and pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::setInlierFraction().
inliers_
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protected |
Inlier points of final transformation as indices into source.
Definition at line 325 of file sample_consensus_prerejective.h.
Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getInliers().
input_features_
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protected |
The source point cloud's feature descriptors.
Definition at line 302 of file sample_consensus_prerejective.h.
Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getSourceFeatures().
k_correspondences_
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protected |
The number of neighbors to use when selecting a random feature correspondence.
Definition at line 312 of file sample_consensus_prerejective.h.
Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getCorrespondenceRandomness(), and pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::setCorrespondenceRandomness().
nr_samples_
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protected |
The number of samples to use during each iteration.
Definition at line 308 of file sample_consensus_prerejective.h.
Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getNumberOfSamples(), and pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::setNumberOfSamples().
target_features_
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protected |
The target point cloud's feature descriptors.
Definition at line 305 of file sample_consensus_prerejective.h.
Referenced by pcl::SampleConsensusPrerejective< PointSource, PointTarget, FeatureT >::getTargetFeatures().
The documentation for this class was generated from the following files:
- pcl/registration/sample_consensus_prerejective.h
- pcl/registration/impl/sample_consensus_prerejective.hpp
© 2009–2012, Willow Garage, Inc.
© 2012–, Open Perception, Inc.
Licensed under the BSD License.
https://pointclouds.org/documentation/classpcl_1_1_sample_consensus_prerejective.html
