C. Kwok, D. Fox, and M. Meila
Adaptive Real-Time Particle
Filters for Robot Localization
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2003
Abstract
Particle filters have recently been applied with great
success to mobile robot localization. This success is mostly due to
their simplicity and their ability to represent arbitrary,
multi-modal densities over a robot's state space. The increased
representational power, however, comes at the cost of higher
computational complexity. In this paper we introduce adaptive
real-time particle filters that greatly increase the performance of
particle filters under limited computational resources. Our
approach improves the efficiency of state estimation by adapting the
size of sample sets on-the-fly. Furthermore, even when large sample
sets are needed to represent a robot's uncertainty, the approach
takes every sensor measurement into account, thereby avoiding the
risk of losing valuable sensor information during the update of the
filter. We demonstrate empirically that this new algorithm
drastically improves the performance of particle filters for robot
localization.
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Bibtex
@INPROCEEDINGS{Thr00Rea,
AUTHOR
= {Kwok, C. and Fox, D. and Meila, M.},
TITLE
= {Adaptive Real-Time Particle Filters for Robot Localization},
BOOKTITLE = {Proc.~of the IEEE International Conference on Robotics \& Automation},
YEAR
= {2003}
}