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. ; This work was supported by projects: 'Supervised learning of industrial scenes by means of an active vision equipped mobile robot.' (J-00063), 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929). This work was supported by the Spanish Council of Science and Technology underProject DPI-2001-2223. ; Peer Reviewed
The effects of partial observability when building fully correlated maps
01.01.2005
doi:10.1109/TRO.2004.842342
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Localization , Mobile robots , SLAM , Mapping
DDC: | 629 |
Assessing partial observability in network sensor location problems
Online Contents | 2014
|Wiley | 2022
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