W. Burgard, D. Fox, D. Hennig, and T. Schmidt

**Estimating the Absolute Position of a Mobile Robot Using**

*Proc. of the Thirteenth National Conference on
Artificial Intelligence (AAAI'96)*

###
Abstract

In order to re-use existing models of the environment
mobile robots must be able to estimate their position and orientation
in such models. Most of the existing methods for position estimation
are based on special purpose sensors or aim at tracking the robot's
position relative to the known starting point. This paper describes
the position probability grid approach to estimating the robot's
absolute position and orientation in a metric model of the
environment. Our method is designed to work with standard sensors and
is independent of any knowledge about the starting point. It is a
Bayesian approach based on certainty grids. In each cell of such a
grid we store the probability that this cell refers to the current
position of the robot. These probabilities are obtained by
integrating the likelihoods of sensor readings over time. Results
described in this paper show that our technique is able to reliably
estimate the position of a robot in complex environments. Our
approach has proven to be robust with respect to inaccurate
environmental models, noisy sensors, and ambiguous situations.

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**Bibtex**

@INPROCEEDINGS{Bur96Est,

AUTHOR
= {Burgard, W. and Fox, D. and Hennig, D. and Schmidt, T.},

TITLE
= {Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids},

BOOKTITLE = {Proc.~of the National Conference on Artificial Intelligence},

YEAR
= {1996}

}

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