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Probabilistic localization for mobile robots using incomplete maps

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4 Author(s)
K. Tanaka ; Kyushu Univ., Japan ; N. Okada ; E. Kondo ; Y. Kimuro

This paper addresses the problem of global localization for mobile robots in changeable environments, i.e. to estimate self-position using a map that is partially or completely different from the environment. It is difficult to detect changes when both of the self-position and the map have large uncertainties. To solve the problem, in this paper, we extend Monte Carlo localization (MCL) and sensor resetting localization (SRL), so as to generate a number of hypotheses about the change as well as the self-position. As a result of tests in a number of environments as well as changes, we found the proposed method is effective even when "rate of changes (ROC)" is high in the environment.

Published in:

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on  (Volume:4 )

Date of Conference:

23-26 Aug. 2004