Detailed Description

Collaboration diagram for Data association:

Classes
struct	mrpt::slam::TDataAssociationResults
	The results from mrpt::slam::data_association. More...

Data association
enum	mrpt::slam::TDataAssociationMethod { mrpt::slam::assocNN = 0, mrpt::slam::assocJCBB }
	Different algorithms for data association, used in mrpt::slam::data_association. More...

enum	mrpt::slam::TDataAssociationMetric { mrpt::slam::metricMaha = 0, mrpt::slam::metricML }
	Different metrics for data association, used in mrpt::slam::data_association For a comparison of both methods see paper: More...

using	mrpt::slam::observation_index_t = size_t
	Used in mrpt::slam::TDataAssociationResults. More...

using	mrpt::slam::prediction_index_t = size_t
	Used in mrpt::slam::TDataAssociationResults. More...

void	mrpt::slam::data_association_full_covariance (const mrpt::math::CMatrixDouble &Z_observations_mean, const mrpt::math::CMatrixDouble &Y_predictions_mean, const mrpt::math::CMatrixDouble &Y_predictions_cov, TDataAssociationResults &results, const TDataAssociationMethod method=assocJCBB, const TDataAssociationMetric metric=metricMaha, const double chi2quantile=0.99, const bool DAT_ASOC_USE_KDTREE=true, const std::vector< prediction_index_t > &predictions_IDs=std::vector< prediction_index_t >(), const TDataAssociationMetric compatibilityTestMetric=metricMaha, const double log_ML_compat_test_threshold=0.0)
	Computes the data-association between the prediction of a set of landmarks and their observations, all of them with covariance matrices - Generic version with prediction full cross-covariances. More...

void	mrpt::slam::data_association_independent_predictions (const mrpt::math::CMatrixDouble &Z_observations_mean, const mrpt::math::CMatrixDouble &Y_predictions_mean, const mrpt::math::CMatrixDouble &Y_predictions_cov, TDataAssociationResults &results, const TDataAssociationMethod method=assocJCBB, const TDataAssociationMetric metric=metricMaha, const double chi2quantile=0.99, const bool DAT_ASOC_USE_KDTREE=true, const std::vector< prediction_index_t > &predictions_IDs=std::vector< prediction_index_t >(), const TDataAssociationMetric compatibilityTestMetric=metricMaha, const double log_ML_compat_test_threshold=0.0)
	Computes the data-association between the prediction of a set of landmarks and their observations, all of them with covariance matrices - Generic version with NO prediction cross-covariances. More...

Typedef Documentation

◆ observation_index_t

using mrpt::slam::observation_index_t = typedef size_t

Used in mrpt::slam::TDataAssociationResults.

Definition at line 55 of file data_association.h.

◆ prediction_index_t

using mrpt::slam::prediction_index_t = typedef size_t

Used in mrpt::slam::TDataAssociationResults.

Definition at line 57 of file data_association.h.

Enumeration Type Documentation

◆ TDataAssociationMethod

enum mrpt::slam::TDataAssociationMethod

Different algorithms for data association, used in mrpt::slam::data_association.

Enumerator
assocNN	Nearest-neighbor.
assocJCBB	JCBB: Joint Compatibility Branch & Bound [Neira, Tardos 2001].

Definition at line 29 of file data_association.h.

◆ TDataAssociationMetric

enum mrpt::slam::TDataAssociationMetric

Different metrics for data association, used in mrpt::slam::data_association For a comparison of both methods see paper:

J.L. Blanco, J. Gonzalez-Jimenez, J.A. Fernandez-Madrigal, "An alternative to the Mahalanobis distance for determining optimal correspondences in data association", IEEE Transactions on Robotics (T-RO), (2012) DOI: 10.1109/TRO.2012.2193706 Draft: http://ingmec.ual.es/~jlblanco/papers/blanco2012amd.pdf

Enumerator
metricMaha	Mahalanobis distance.
metricML	Matching likelihood (See TDataAssociationMetric for a paper explaining this metric)

Definition at line 45 of file data_association.h.

Function Documentation

◆ data_association_full_covariance()

void mrpt::slam::data_association_full_covariance	(	const mrpt::math::CMatrixDouble &	Z_observations_mean,
		const mrpt::math::CMatrixDouble &	Y_predictions_mean,
		const mrpt::math::CMatrixDouble &	Y_predictions_cov,
		TDataAssociationResults &	results,
		const TDataAssociationMethod	method = `assocJCBB`,
		const TDataAssociationMetric	metric = `metricMaha`,
		const double	chi2quantile = `0.99`,
		const bool	DAT_ASOC_USE_KDTREE = `true`,
		const std::vector< prediction_index_t > &	predictions_IDs = `std::vector<prediction_index_t>()`,
		const TDataAssociationMetric	compatibilityTestMetric = `metricMaha`,
		const double	log_ML_compat_test_threshold = `0.0`
	)

Computes the data-association between the prediction of a set of landmarks and their observations, all of them with covariance matrices - Generic version with prediction full cross-covariances.

Implemented methods include (see TDataAssociation)

NN: Nearest-neighbor
JCBB: Joint Compatibility Branch & Bound [Neira, Tardos 2001]

With both a Mahalanobis-distance or Matching-likelihood metric. For a comparison of both methods, see paper:

J.L. Blanco, J. Gonzalez-Jimenez, J.A. Fernandez-Madrigal, "An alternative to the Mahalanobis distance for determining optimal correspondences in data association", IEEE Transactions on Robotics (T-RO), (2012) DOI: 10.1109/TRO.2012.2193706 Draft: http://ingmec.ual.es/~jlblanco/papers/blanco2012amd.pdf

Parameters

Z_observations_mean	[IN] An MxO matrix with the M observations, each row containing the observation "mean".
Y_predictions_mean	[IN] An NxO matrix with the N predictions, each row containing the mean of one prediction.
Y_predictions_cov	[IN] An NOxNO matrix with the full covariance matrix of all the N predictions.
results	[OUT] The output data association hypothesis, and other useful information.
method	[IN, optional] The selected method to make the associations.
chi2quantile	[IN, optional] The threshold for considering a match between two close Gaussians for two landmarks, in the range [0,1]. It is used to call mrpt::math::chi2inv
use_kd_tree	[IN, optional] Build a KD-tree to speed-up the evaluation of individual compatibility (IC). It's perhaps more efficient to disable it for a small number of features. (default=true).
predictions_IDs	[IN, optional] (default:none) An N-vector. If provided, the resulting associations in "results.associations" will not contain prediction indices "i", but "predictions_IDs[i]".

See also: data_association_independent_predictions, data_association_independent_2d_points, data_association_independent_3d_points

Definition at line 297 of file data_association.cpp.

References mrpt::math::CMatrixDynamic< T >::asEigen(), ASSERT_, mrpt::slam::assocJCBB, mrpt::slam::assocNN, mrpt::math::chi2inv(), mrpt::math::CMatrixDynamic< T >::cols(), mrpt::math::MatrixVectorBase< Scalar, Derived >::isSquare(), mrpt::slam::TAuxDataRecursiveJCBB::length_O, mrpt::math::mahalanobisDistance2AndLogPDF(), mrpt::slam::metricMaha, mrpt::slam::metricML, MRPT_END, MRPT_START, mrpt::slam::TAuxDataRecursiveJCBB::nObservations, mrpt::slam::TAuxDataRecursiveJCBB::nPredictions, results, mrpt::math::CMatrixDynamic< T >::rows(), THROW_EXCEPTION, and val.

Referenced by mrpt::slam::data_association_independent_predictions(), mrpt::slam::CRangeBearingKFSLAM2D::OnGetObservationsAndDataAssociation(), and mrpt::slam::CRangeBearingKFSLAM::OnGetObservationsAndDataAssociation().

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◆ data_association_independent_predictions()

void mrpt::slam::data_association_independent_predictions	(	const mrpt::math::CMatrixDouble &	Z_observations_mean,
		const mrpt::math::CMatrixDouble &	Y_predictions_mean,
		const mrpt::math::CMatrixDouble &	Y_predictions_cov,
		TDataAssociationResults &	results,
		const TDataAssociationMethod	method = `assocJCBB`,
		const TDataAssociationMetric	metric = `metricMaha`,
		const double	chi2quantile = `0.99`,
		const bool	DAT_ASOC_USE_KDTREE = `true`,
		const std::vector< prediction_index_t > &	predictions_IDs = `std::vector<prediction_index_t>()`,
		const TDataAssociationMetric	compatibilityTestMetric = `metricMaha`,
		const double	log_ML_compat_test_threshold = `0.0`
	)

Computes the data-association between the prediction of a set of landmarks and their observations, all of them with covariance matrices - Generic version with NO prediction cross-covariances.