This paper presents a new particle method, with stochastic parameter estimation, to solve the SLAM problem. The underlying algorithm is rooted on a solid probabilistic foundation and is guaranteed to converge asymptotically, unlike many existing popular approaches. Moreover, it is efficient in storage and computation. The new algorithm carries out filtering only in the marginal filtering space, thereby allowing for the recursive computation of low variance estimates of the map. The paper provides mathematical arguments and empirical evidence to substantiate the fact that the new method represents an improvement over the existing particle filtering approaches for SLAM, which work on the joint path state space.
['Ruben Martinez-Cantin', 'N. de Freitas', 'J.A. Castellanos']
Analysis of Particle Methods for Simultaneous Robot Localization and Mapping and a New Algorithm: Marginal-SLAM