New online EM algorithms for general hidden markov models. application to the SLAM problem

Authors:
Sylvain Le Corff;Gersende Fort;Eric Moulines
Affiliations:
LTCI, CNRS and TELECOM ParisTech, Paris Cedex 13, France;LTCI, CNRS and TELECOM ParisTech, Paris Cedex 13, France;LTCI, CNRS and TELECOM ParisTech, Paris Cedex 13, France
Venue:
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Year:
2012

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FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence

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Abstract

In this contribution, new online EM algorithms are proposed to perform inference in general hidden Markov models. These algorithms update the parameter at some deterministic times and use Sequential Monte Carlo methods to compute approximations of filtering distributions. Their convergence properties are addressed in [9] and [10]. In this paper, the performance of these algorithms are highlighted in the challenging framework of Simultaneous Localization and Mapping.