W. Burgard, A. Derr, D. Fox, and A.B.Cremers
Integrating Global Position Estimation and Position Tracking for Mobile Robots: The Dynamic Markov Localization Approach
Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'98)
Abstract
Localization is one of the fundamental problems of mobile robots.
In order to efficiently perform useful tasks such as office
delivery, mobile robots must know their position in their
environment. Existing approaches can be distinguished according to
the type of localization problem they are designed to solve.
Tracking techniques aim at monitoring the robot's position. They
assume that the position is initially known and cannot recover from
situations in which they lost track of the robot's position. Global
localization techniques, on the other hand, are able to estimate the
robot's position under complete uncertainty. In this paper we
present the dynamic Markov localization technique as a uniform
approach to position estimation, which is able (1) to globally
estimate the position of the robot, (2) to efficiently track its
position whenever the robot's certainty is high, and (3) to detect
and recover from localization failures. The approach has been
implemented and intensively tested in real-world environments. We
present several experiments illustrating the strength of our method.
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Bibtex
@INPROCEEDINGS{Bur98Int,
AUTHOR
= {Burgard, W. and Derr, A. and Fox, D. and Cremers, A.B.},
TITLE
= {Integrating global position estimation and position tracking for mobile robots: the {D}ynamic {M}arkov {L}ocalization approach},
BOOKTITLE = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and Systems},
YEAR
= {1998}
}
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