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 canbe
distinguished according to the type of localization problem they aredesigned
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 localizationtechniques,
on the other hand, are able to estimate the robot's positionunder complete
uncertainty. In this paper we present the dynamic Markovlocalization technique
as a uniform approach to position estimation, whichis able (1) to globally
estimate the position of the robot, (2) to efficientlytrack its position
whenever the robot's certainty is high, and (3) to detect and recover from
localization failures. The approach has been implementedand intensively
tested in real-world environments. We present several experimentsillustrating
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 mobilerobots: the {D}ynamic {M}arkov {L}ocalization
approach},
BOOKTITLE = {Proc.~of the IEEE/RSJ InternationalConference on Intelligent
Robots and Systems},
YEAR = {1998}
}