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 ltering distribu- tions. In this paper, the performance of these algorithms are highlighted in the chal- lenging framework of Simultaneous Localization and Mapping.