J.-S. Gutmann, W. Burgard, D. Fox, and K. Konolige
An experimental comparison
of localization methods
Best Paper Award
Proc. of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS'98)
Abstract
Localization is the process of updating the pose of a robot in an
environment, based on sensor readings. In this experimental study, we
compare two recent methods for localization of indoor mobile robots:
Markov localization, which uses a probability distribution across a
grid of robot poses; and scan matching, which uses Kalman filtering
techniques based on matching sensor scans. Both these techniques are
dense matching methods, that is, they match dense sets of
environment features to an a priori map. To arrive at results for a
range of situations, we utilize several different types of
environments, and add noise to both the dead-reckoning and the
sensors. Analysis shows that, roughly, the scan-matching techniques
are more efficient and accurate, but Markov localization is better
able to cope with large amounts of noise. These results suggest
hybrid methods that are efficient, accurate and robust to noise.
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Bibtex
@INPROCEEDINGS{Gut98Exp,
AUTHOR
= {Gutmann, J.-S. and Burgard, W. and Fox, D. and Konolige, K.},
TITLE
= {An Experimental Comparison of Localization Methods},
BOOKTITLE = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and Systems},
YEAR
= {1998}
}
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