MONTE CARLO LOCALIZATION IN HIGHLY SYMMETRIC ENVIRONMENTS

Stephan Sehestedt, Frank E. Schneider

Abstract

The localization problem is a central issue in mobile robotics. Monte Carlo Localization (MCL) is a popular method to solve the localization problem for mobile robots. However, usual MCL has some shortcomings in terms of computational complexity, robustness and the handling of highly symmetric environments. These three issues are adressed in this work. We present three Monte Carlo localization algorithms as a solution to these problems. The focus lies on two of these, which are especially suitable for highly symmetric environments, for which we introduce two-stage sampling as the resampling scheme.

References

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Paper Citation


in Harvard Style

Sehestedt S. and E. Schneider F. (2006). MONTE CARLO LOCALIZATION IN HIGHLY SYMMETRIC ENVIRONMENTS . In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-972-8865-60-3, pages 249-254. DOI: 10.5220/0001215802490254


in Bibtex Style

@conference{icinco06,
author={Stephan Sehestedt and Frank E. Schneider},
title={MONTE CARLO LOCALIZATION IN HIGHLY SYMMETRIC ENVIRONMENTS},
booktitle={Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2006},
pages={249-254},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001215802490254},
isbn={978-972-8865-60-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - MONTE CARLO LOCALIZATION IN HIGHLY SYMMETRIC ENVIRONMENTS
SN - 978-972-8865-60-3
AU - Sehestedt S.
AU - E. Schneider F.
PY - 2006
SP - 249
EP - 254
DO - 10.5220/0001215802490254