  The aim of this paper is to present a new method for visual place
recognition. Our system combines global image characterization and visual
words, which allows to use efficient Bayesian filtering methods to integrate
several images. More precisely, we extend the classical HMM model with
techniques inspired by the field of Natural Language Processing. This paper
presents our system and the Bayesian filtering algorithm. The performance of
our system and the influence of the main parameters are evaluated on a standard
database. The discussion highlights the interest of using such models and
proposes improvements.
