W. Burgard, D. Fox, H. Jans, C. Matenar, and S. Thrun
Sonar-Based Mapping with Mobile
Robots Using EM
Proc. of the 16th International Conference on Machine
Learning (ICML'99)
Abstract
In this paper we present a method for learning maps with mobile robots
equipped with range finders. Our method builds on an approach previously
developed by the authors, which uses EM to solve the concurrent mapping
and localization problem (constrained maximum likelihood estimation). In
contrast to other techniques which either relied on predefined landmarks
or used highly accurate sensors, our approach is able to fully exploit
the rich nature of range data and to deal with noisy information coming,
for example, from ultrasound sensors. During EM it uses a layered representation
of maps. It operates in two stages: first, small, local maps are learned
under the assumption that odometry is locally correct. EM is then applied
to to estimate the positions of these local maps. Finally, the local maps
are integrated into one global map using Bayes rule. Experimental results
demonstrate that our approach is well suited for constructing large maps
of typical indoor environments using sensors as inaccurate as sonars.
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Bibtex
@INPROCEEDINGS{Bur99Son,
AUTHOR = {Burgard,
W. and Fox, D. and Jans, H. and Matenar, C. and Thrun, S.},
TITLE
= {Sonar-Based Mapping with Mobile Robots Using {EM}},
YEAR
= {1999},
BOOKTITLE = Proc.~of the International
Conference on Machine Learning
}
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