J.-S. Gutmann, W. Burgard, D. Fox, and K. Konolige
An experimental comparison of
localization methods
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
recentmethods for localization of indoor mobile robots: Markov localization,
whichuses a probability distribution across a grid of robot poses; and scan
matching,which uses Kalman filtering techniques based on matching sensor
scans. Boththese techniques are dense matching methods, that is, they
match dense setsof environment features to an a priori map. To arrive
at results for arangeof situations, we utilize several different types ofenvironments,
and addnoise to both the dead-reckoning and the sensors. Analysis shows
that, roughly,the scan-matching techniques are more efficient and accurate,
but Markov localizationis better able to cope with large amounts of noise.
These results suggesthybrid 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 InternationalConference on Intelligent
Robots and Systems},
YEAR = {1998}
}