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

Estimating the Absolute Position of a Mobile Robot Using Position Probability Grids

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 estimatingthe 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 ofany knowledge about the starting point. It is a Bayesian approach based oncertainty grids.  In each cell of such a grid we store the probability thatthis cell refers to the current position of the robot.  These probabilitiesare obtained by integrating the likelihoods of sensor readings over time.  Results described in this paper show that our technique is able to reliablyestimate the position of a robot in complex environments.  Our approach hasproven to be robust with respect to inaccurate environmental models, noisysensors, and ambiguous situations.

Download

Full paper [.pdf] (154 kb, 6 pages)

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 ProbabilityGrids},
  BOOKTITLE = {Proc.~of the National Conferenceon Artificial Intelligence},
  YEAR      = {1996}
}