Box Particle Filtering for SLAM with Bounded Errors

This paper proposes a set-membership based method for simultaneous localization and mapping. A box particle filter is exploited and improved to estimate robot states and feature positions. An interval constraint propagation is used to reduce box sizes, i.e., to decrease the uncertainty of the estimates. Buffers are also used to get q-satisfied results when empty estimates arise, on the one hand. On the other hand, historical data are used to improve the estimation through buffer contraction. Illustrations of the proposed method are given over simulations and experiments, with comparisons with a particle filter based method. The results show that the proposed method can reach the same simultaneous localization and mapping accuracy as a particle filter based method but with fewer particles. Moreover, this approach is comparatively more robust to system and measurement noises.