Sylvain Le Corff

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In this paper, a new algorithm — namely the onlineEM-SLAM — is proposed to solve the simultaneous localization and mapping problem (SLAM). The mapping problem is seen as an instance of inference in latent models, and the localization part is dealt with a particle approximation method. This new technique relies on an online version of the(More)
Approximating fixed-interval smoothing distributions using particle-based methods is a well-known issue in statistical inference when operating on general state-space hidden Markov models (HMM). In this paper we focus on the computation of path-space smoothed additive functionals. More precisely, this contribution provides new results on the forward(More)
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