D. Fox, W. Burgard, S. Thrun, and A.B. Cremers
Position Estimation for Mobile Robots in Dynamic Environments
Proc. of the Fifteenth National Conference on Artificial Intelligence (AAAI-98)
Abstract
For mobile robots to be successful, they have to
navigate safely in populated and dynamic environments. While recent
research has led to a variety of localization methods that can track
robots well in {\em static} environments, we still lack methods that
can robustly localize mobile robots in dynamic environments, where,
for example, people may block the robot's sensors for extensive
periods of time. This paper proposes a family of probabilistic
algorithms that can localize mobile robots even in densely populated
environments. These algorithms are based on Markov localization,
which estimates the location of a robot probabilistically. A novel
entropy-based filter is employed for determining the "believability"
of a sensor reading, thereby filtering out sensor readings that are
corrupted by humans or unexpected changes in the environment. The
technique was recently implemented and applied as part of an
installation, in which a mobile robot gave interactive tours to
visitors of the ``Deutsches Museum Bonn.'' Extensive empirical tests
involving datasets recorded during peak traffic hours in the museum
demonstrate that this approach is able to accurately estimate the
robot's position in more than 99\% of the cases even in such highly
dynamic environments.
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Bibtex
@INPROCEEDINGS{Fox98Pos,
AUTHOR
= {Fox, D. and Burgard, W. and Thrun, S. and Cremers, A.B.},
TITLE
= {Position Estimation for Mobile Robots in Dynamic Environments},
YEAR
= {1998},
BOOKTITLE = {Proc.~of the National Conference on Artificial Intelligence}
}
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