Data association implementation that uses the Negative Log-Likehood as criterion to make observation matches. More...

Inheritance diagram for MaximumLikelihoodNLL:

Collaboration diagram for MaximumLikelihoodNLL:

Public Member Functions
	MaximumLikelihoodNLL (const DataAssociationParameters &params)

	~MaximumLikelihoodNLL ()=default

Eigen::VectorXi	associate (const Eigen::VectorXd &landmarks, const Eigen::VectorXd &observations, const Eigen::MatrixXd &covariance, const Eigen::VectorXd &observation_confidences) const override
	This function associates the landmarks with the observations.

Public Member Functions inherited from DataAssociationModel
virtual int	associate_n_filter (const std::vector< common_lib::structures::Cone > &perception_map, Eigen::VectorXf &_x_vector_, Eigen::MatrixXf &_p_matrix_, std::vector< int > &matched_ids, std::vector< Eigen::Vector2f > &matched_cone_positions, std::vector< Eigen::Vector2f > &new_features, ObservationModel *observation_model) const =0
	Associate the observed landmarks to the expected landmarks and update the state vector and the covariance matrix.

	DataAssociationModel (float max_landmark_distance)

virtual	~DataAssociationModel ()=default

	DataAssociationModel ()=default

	DataAssociationModel (DataAssociationParameters params)

virtual	~DataAssociationModel ()=default

Private Attributes
Eigen::Matrix2d	observation_noise_covariance_matrix_

Additional Inherited Members
Protected Member Functions inherited from DataAssociationModel
float	get_max_landmark_distance () const

Protected Attributes inherited from DataAssociationModel
DataAssociationParameters	_params_

Detailed Description

Data association implementation that uses the Negative Log-Likehood as criterion to make observation matches.

Definition at line 13 of file maximum_likelihood_nll.hpp.

Constructor & Destructor Documentation

MaximumLikelihoodNLL::MaximumLikelihoodNLL ( const DataAssociationParameters & params )

inline

Definition at line 17 of file maximum_likelihood_nll.hpp.

MaximumLikelihoodNLL::~MaximumLikelihoodNLL ( )

default

Eigen::VectorXi MaximumLikelihoodNLL::associate	(	const Eigen::VectorXd &	landmarks,
		const Eigen::VectorXd &	observations,
		const Eigen::MatrixXd &	covariance,
		const Eigen::VectorXd &	observation_confidences
	)		const

overridevirtual

This function associates the landmarks with the observations.

Parameters

landmarks	Landmarks in the form of [x1, y1, x2, y2, ...] in the global frame
observations	Observations in the form of [x1, y1, x2, y2, ...] in the global frame
covariance	Covariance matrix of the landmark vector
observation_confidences	Confidence in the observations in the same order as the observations

Returns: Eigen::VectorXi Each entry corresponds to an observation and contains the index of the landmark that the observation is associated with in the landmark vector (x coordinate). If the observation is considered new, the entry is -1. If the observation is considered an outlier, the entry is -2.

Definition at line 7 of file maximum_likelihood_nll.cpp.

Here is the caller graph for this function:

Eigen::Matrix2d MaximumLikelihoodNLL::observation_noise_covariance_matrix_

private

Definition at line 14 of file maximum_likelihood_nll.hpp.

The documentation for this class was generated from the following files:

src/perception_sensor_lib/include/perception_sensor_lib/data_association/maximum_likelihood_nll.hpp
src/perception_sensor_lib/src/data_association/maximum_likelihood_nll.cpp