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. More...
typedef size_t	mrpt::slam::observation_index_t
	Used in mrpt::slam::TDataAssociationResults.
typedef size_t	mrpt::slam::prediction_index_t
	Used in mrpt::slam::TDataAssociationResults.
void SLAM_IMPEXP	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.
void SLAM_IMPEXP	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.

Typedef Documentation

typedef size_t mrpt::slam::observation_index_t

Used in mrpt::slam::TDataAssociationResults.

Definition at line 66 of file data_association.h.

typedef size_t mrpt::slam::prediction_index_t

Used in mrpt::slam::TDataAssociationResults.

Definition at line 67 of file data_association.h.

Enumeration Type Documentation

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 52 of file data_association.h.

enum mrpt::slam::TDataAssociationMetric

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

Enumerator:

metricMaha	Mahalanobis distance.
metricML	Matching likelihood (See paper: http://www.mrpt.org/Paper:Matching_Likelihood )

Definition at line 60 of file data_association.h.

Function Documentation

void SLAM_IMPEXP 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 (See paper: http://www.mrpt.org/Paper:Matching_Likelihood )

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 N·OxN·O 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

void SLAM_IMPEXP 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.