S. Thrun, D. Fox, and W. Burgard
Monte Carlo Localization with
Mixture Proposal distributions
Proc. of the National Conference on Artificial Intelligence
(AAAI)
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 {C}arlo Localization
with Mixture Proposal Distributions},
YEAR = {2000},
BOOKTITLE = AAAI
}