Publication

Conditions for suboptimal filter stability in SLAM

Conference Article

Conference

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Edition

2004

Pages

27-32

Doc link

http://dx.doi.org/10.1109/IROS.2004.1389324

File

Download the digital copy of the doc pdf document

Abstract

In this article, we show marginal stability in SLAM, guaranteeing convergence to a non-zero mean state error estimate bounded by a constant value. Moreover, marginal stability guarantees also convergence of the Riccati equation of the one-step ahead state error covariance to at least one psd steady state solution. In the search for real time implementations of SLAM, covariance inflation methods produce a suboptimal filter that eventually may lead to the computation of an unbounded state error covariance. We provide tight constraints in the amount of decorrelation possible, to guarantee convergence of the state error covariance, and at the same time, a linear-time implementation of SLAM.

Categories

robots.

Scientific reference

T. Vidal-Calleja, J. Andrade-Cetto and A. Sanfeliu. Conditions for suboptimal filter stability in SLAM, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004, Sendai, Japan, in Intelligent Robots and Systems, pp. 27-32, 2004, IEEE, Barcelona, Espanya.