Package javaslam.slam

Contains data structures and algorithms for filtering approaches to the Simultaneous Localization and Mapping (SLAM) problem (with known data association).

See:
Description

Interface Summary

Model	Represents a SLAM model.
NonlinearSLAMFilter	An interface implemented by classes that implement nonlinear filters for the Simultaneous Localization and Mapping (SLAM) problem.
SLAMFilter	An interface implemented by filters for the Simultaneous Localization and Mapping (SLAM) problem.

Class Summary

AbstractSLAMFilter	An abstract class extended by classes that implement filters for the Simultaneous Localization and Mapping (SLAM) problem.
ExampleModel	A SLAM model for a robot that can rotate about its vertical axis and translate along its current heading and which receives differential odometry measurements and range-bearing landmark measurements.
InformationSLAMFilter	An information filter for the Simultaneous Localization and Mapping (SLAM) problem.
KalmanSLAMFilter	A Kalman filter for the Simultaneous Localization and Mapping (SLAM) problem.
LGSLAMFilter	A linear-Gaussian filter for the Simultaneous Localization and Mapping (SLAM) problem.
LGSLAMFilterCanvas
LinearizedSLAMFilter	A nonlinear filter for the Simultaneous Localization and Mapping SLAM problem that uses a linear-Gaussian filter with a technique for linearizing nonlinear motion and measurement models.
TJTSLAMFilter	A thin junction tree filter for the Simultaneous Localization and Mapping (SLAM) problem.

Package javaslam.slam Description

Contains data structures and algorithms for filtering approaches to the Simultaneous Localization and Mapping (SLAM) problem (with known data association). This package contains three types of SLAM filters for linear-Gaussian environments:

• the KalmanSLAMFilter, which represents its belief state using a full multivariate Gaussian in the moment parameterization;
• the InformationSLAMFilter, which represents its belief state using a full multivariate Gaussian in the canonical parameterization; and
• the TJTSLAMFilter of (Paskin, 2002), which represents its belief state using a thin junction tree.

In addition, it contains two types of filters for nonlinear SLAM environments:

• the LinearizedSLAMFilter, which couples one of the linear-Gaussian filters above with a Linearization technique; and