S. Thrun, D. Fox, and W. Burgard
Monte Carlo Localization With Mixture Proposal Distribution
Proc. of the National Conference on Artificial
Intelligence (AAAI), 2000
Abstract
Monte Carlo localization (MCL) is a Bayesian algorithm
for mobile robot localization based on particle filters, which has
enjoyed great practical success. This paper points out a limitation of
MCL which is counter-intuitive, namely that better sensors can yield
worse results. An analysis of this problem leads to the formulation of
a new proposal distribution for the Monte Carlo sampling
step. Extensive experimental results with physical robots suggest that
the new algorithm is significantly more robust and accurate than plain
MCL. Obviously, these results transcend beyond mobile robot
localization and apply to a range of particle filter applications.
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Bibtex
@INPROCEEDINGS{Thr00Mon,
AUTHOR
= {Thrun, S. and Fox, D. and Burgard, W.},
TITLE
= {Monte Carlo Localization With Mixture Proposal Distribution},
YEAR
= {2000},
BOOKTITLE = {Proc.~of the National Conference on Artificial Intelligence}
}
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