A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping

Peter Torma, András György, Csaba Szepesvári
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:852-859, 2010.

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 this Paper

BibTeX
@InProceedings{pmlr-v9-torma10a, 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}, pages = {852--859}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/torma10a/torma10a.pdf}, url = {https://proceedings.mlr.press/v9/torma10a.html}, 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.} }
Endnote
%0 Conference Paper %T A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping %A Peter Torma %A András György %A Csaba Szepesvári %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-torma10a %I PMLR %P 852--859 %U https://proceedings.mlr.press/v9/torma10a.html %V 9 %X 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.
RIS
TY - CPAPER TI - A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping AU - Peter Torma AU - András György AU - Csaba Szepesvári BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-torma10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 852 EP - 859 L1 - http://proceedings.mlr.press/v9/torma10a/torma10a.pdf UR - https://proceedings.mlr.press/v9/torma10a.html AB - 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. ER -
APA
Torma, P., György, A. & Szepesvári, C.. (2010). A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:852-859 Available from https://proceedings.mlr.press/v9/torma10a.html.

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