F. Dellaert, D. Fox, W. Burgard, and S. Thrun
Monte Carlo Localization For Mobile Robots
Proc. of the
IEEE International Conference on Robotics and Automation (ICRA'99)
Abstract
To navigate reliably in indoor environments, a
mobile robot must know where it is. Thus, reliable position estimation
is a key problem in mobile robotics. We believe that probabilistic
approaches are among the most promising candidates to providing a
comprehensive and real-time solution to the robot localization
problem. However, current methods still face considerable hurdles. In
particular, the problems encountered are closely related to the type
of representation used to represent probability densities over the
robot's state space. Recent work on Bayesian filtering with
particle-based density representations opens up a new approach for
mobile robot localization, based on these principles. In this paper
we introduce the Monte Carlo Localization method, where we represent
the probability density involved by maintaining a set of samples that
are randomly drawn from it. By using a sampling-based representation
we obtain a localization method that can represent arbitrary
distributions. We show experimentally that the resulting method is
able to efficiently localize a mobile robot without knowledge of its
starting location. It is faster, more accurate and less
memory-intensive than earlier grid-based methods.
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Bibtex
@INPROCEEDINGS{Del99Mon,
AUTHOR
= {Dellaert, F. and Fox, D. and Burgard, W. and Thrun, S.},
TITLE
= {Monte Carlo Localization For Mobile Robots},
BOOKTITLE = {Proc.~of the IEEE International Conference on Robotics \& Automation},
YEAR
= {1998}
}
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