PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping
Péter Torma, Andras Gyorgy and Csaba Szepesvari
In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010) JMLR Workshop and Conference Proceedings Series , 9 . (2010) Journal of Machine Learning Research , pp. 852-859.

Abstract

A Markov-Chain Monte Carlo based algorithm is provided to solve the Simultaneous Localization and Mapping (SLAM) problem with general dynamical and observation models under open-loop control and provided that the map-representation is finite dimensional. To our knowledge this is the first provably consistent yet (close-to) practical solution to this problem. The superiority of our algorithm over alternative SLAM algorithms is demonstrated in a difficult loop closing situation.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6135
Deposited By:Andras Gyorgy
Deposited On:08 March 2010