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Sensor planning for mobile robot localization - a hierarchical approach using Bayesian network and particle filter

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2 Author(s)
Hongjun Zhou ; Chuo Univ., Japan ; S. Sakane

In this paper we propose a hierarchical approach to solving sensor planning for the global localization of a mobile robot. Our system consists of two subsystems: a lower and a higher layer. The lower layer uses a particle filter to evaluate the posterior probability of the localization. When the particles converge into clusters, the higher layer starts particle clustering and sensor planning to generate an optimal sensing action sequence for the localization. The higher layer uses a Bayesian network for the probabilistic inference. The sensor planning takes into account both localization belief and sensing cost. We conducted simulations and actual robot experiments to validate our proposed approach.

Published in:

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems

Date of Conference:

2-6 Aug. 2005