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A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping
Peter Torma, András György, Csaba SzepesváriProceedings 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 - CPAPERTI - A Markov-Chain Monte Carlo Approach to Simultaneous Localization and MappingAU - Peter TormaAU - András GyörgyAU - Csaba SzepesváriBT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and StatisticsDA - 2010/03/31ED - Yee Whye TehED - Mike TitteringtonID - pmlr-v9-torma10aPB - PMLRDP - Proceedings of Machine Learning ResearchVL - 9SP - 852EP - 859L1 - http://proceedings.mlr.press/v9/torma10a/torma10a.pdfUR - https://proceedings.mlr.press/v9/torma10a.htmlAB - 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, inProceedings of Machine Learning Research 9:852-859 Available from https://proceedings.mlr.press/v9/torma10a.html.