IRI - The effects of partial observability when building fully correlated maps

Publication

The effects of partial observability when building fully correlated maps

Journal Article (2005)

Journal

IEEE Transactions on Robotics

Pages

771-777

Volume

Number

Doc link

http://dx.doi.org/10.1109/TRO.2004.842342

File

Download the digital copy of the doc pdf document

Authors

Projects associated

ANNA: Supervised learning of industrial scenes by means of an active vision equipped mobile robot.
NAVROB: Integration of robust perception, learning, and navigation systems in mobile robotics

Abstract

This article presents an analysis of the fully correlated approach to simultaneous localization and map building problem from a control systems theory point of view, both for linear and nonlinear vehicle models. We show how partial observability hinders full reconstructibility of the state space, making the final map estimate dependent on the initial observations. Nevertheless, marginal filter stability guarantees convergence of the state error covariance to a positive semi-definite covariance matrix. By characterizing the form of the total Fisher information we are able to determine the unobservable state space directions. Moreover, we give a closed form expression that links the amount of reconstruction error to the number of landmarks used. The analysis allows the formulation of measurement models that make SLAM observable.

Author keywords

mobile robots, localization, mapping, estimation, SLAM

Scientific reference

J. Andrade-Cetto and A. Sanfeliu. The effects of partial observability when building fully correlated maps. IEEE Transactions on Robotics, 21(4): 771-777, 2005.