Analysis and improvement of the consistency of extended Kalman filter based SLAM

In this work, we study the inconsistency of EKF- based SLAM from the perspective of observability. We analytically prove that when the Jacobians of the state and measurement models are evaluated at the latest state estimates during every time step, the linearized error-state system model of the EKF-...

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Bibliographic Details
Published in2008 IEEE International Conference on Robotics and Automation pp. 473 - 479
Main Authors Huang, G.P., Mourikis, A.I., Roumeliotis, S.I.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2008
Subjects
Computer science
Filters
Jacobian matrices
Observability
Robotics and automation
Robots
Simultaneous localization and mapping
State estimation
Time measurement
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Summary:In this work, we study the inconsistency of EKF- based SLAM from the perspective of observability. We analytically prove that when the Jacobians of the state and measurement models are evaluated at the latest state estimates during every time step, the linearized error-state system model of the EKF- based SLAM has observable subspace of dimension higher than that of the actual, nonlinear, SLAM system. As a result, the covariance estimates of the EKF undergo reduction in directions of the state space where no information is available, which is a primary cause of inconsistency. To address this issue, a new "first estimates Jacobian" (FEJ) EKF is proposed, which is shown to perform better in terms of consistency. In the FEJ- EKF, the filter Jacobians are calculated using the first-ever available estimates for each state variable, which insures that the observable subspace of the error-state system model is of the same dimension as that of the underlying nonlinear SLAM system. The theoretical analysis is validated through extensive simulations.
ISBN:1424416469
9781424416462
ISSN:1050-4729
DOI:10.1109/ROBOT.2008.4543252