Bayesian calibration for Monte Carlo localization
- Armita Kaboli
- Michael Bowling, University of Alberta
- Petr Musilek
Localization is a fundamental challenge for autonomous robotics. Although accurate and efficient techniques now exist for solving this problem, they require explicit probabilistic models of the robot’s motion and sensors. These models are usually obtained from time-consuming and error-prone measurement or tedious manual tuning. In this paper we examine automatic calibration of sensor and motion models from a Bayesian perspective. We introduce an efficient MCMC procedure for sampling from the posterior distribution of the model parameters. We also present a novel extension of particle filters to make use of our posterior parameter samples. Finally, we demonstrate our approach both in simulation and on a physical robot. Our results demonstrate effective inference of model parameters as well as a paradoxical result that using posterior parameter samples can produce more accurate position estimates than the true parameters.
Citation
A. Kaboli, M. Bowling, P. Musilek. "Bayesian calibration for Monte Carlo localization". National Conference on Artificial Intelligence (AAAI), Boston, Massachusetts, USA, pp 964-969, January 2006.| Keywords: | machine learning |
| Category: | In Conference |
BibTeX
@incollection{Kaboli+al:AAAI06,
author = {Armita Kaboli and Michael Bowling and Petr Musilek},
title = {Bayesian calibration for Monte Carlo localization},
Pages = {964-969},
booktitle = {National Conference on Artificial Intelligence (AAAI)},
year = 2006,
}Last Updated: April 24, 2007Submitted by William Thorne
