TY - GEN
T1 - New online EM algorithms for general hidden Markov models. Application to the SLAM problem
AU - Le Corff, Sylvain
AU - Fort, Gersende
AU - Moulines, Eric
PY - 2012/2/27
Y1 - 2012/2/27
N2 - 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.
AB - 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.
KW - Hidden Markov models
KW - Online Expectation-Maximization
KW - SLAM
KW - Statistical inference
UR - https://www.scopus.com/pages/publications/84857288746
U2 - 10.1007/978-3-642-28551-6_17
DO - 10.1007/978-3-642-28551-6_17
M3 - Conference contribution
AN - SCOPUS:84857288746
SN - 9783642285509
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 131
EP - 138
BT - Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
T2 - 10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012
Y2 - 12 March 2012 through 15 March 2012
ER -