Observability analysis of SLAM using fisher information matrix

This paper presents a new technique for evaluating the observability of the simultaneous localization and mapping (SLAM) problem. The state vector of an estimation theoretic formulation of the SLAM problem is recast to include all robot poses from which the measurements are made. This converts SLAM...

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Bibliographic Details
Published in2008 10th International Conference on Control Automation Robotics and Vision pp. 1242 - 1247
Main Authors Zhan Wang, Dissanayake, G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2008
Subjects
Control systems
Estimation theory
Fisher Information Matrix
Information analysis
Mobile robots
Observability
Robot kinematics
Robot sensing systems
Robotics and automation
Simultaneous localization and mapping
SLAM
State estimation
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Summary:This paper presents a new technique for evaluating the observability of the simultaneous localization and mapping (SLAM) problem. The state vector of an estimation theoretic formulation of the SLAM problem is recast to include all robot poses from which the measurements are made. This converts SLAM to a problem of estimating a set of unknown, constant random variables. Fisher Information Matrix of the resulting static estimation problem is derived and analyzed to examine the observability of SLAM. Outcomes of the analysis and comparisons to the observability analysis presented in recent literature are presented. Proposed technique makes it possible to analyze the observability of a range of SLAM problems with ease.
ISBN:9781424422869
1424422868
DOI:10.1109/ICARCV.2008.4795699