A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:852-859, 2010.
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.