A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping
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.
Cite
Text
Torma et al. "A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Torma et al. "A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/torma2010aistats-markovchain/)BibTeX
@inproceedings{torma2010aistats-markovchain,
title = {{A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping}},
author = {Torma, Peter and György, András and Szepesvári, Csaba},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {852-859},
volume = {9},
url = {https://mlanthology.org/aistats/2010/torma2010aistats-markovchain/}
}