The H∞ FastSLAM framework

FastSLAM is a framework using a Rao-Blackwellized particle filter. However, the performance of FastSLAM depends on correct a priori knowledge of the process and measurement noise covariance matrices (Q_t and R_t) that are in most applications unknown. On the other hand, an incorrect a priori knowledge of Q_t and R_t may seriously degrade the performance of FastSLAM. To solve these problems, this paper presents H_∞ FastSLAM. In this approach, H_∞ particle filter is used for the mobile robot position estimation and H_∞ filter is used for the feature location´s estimation. The H_∞ FastSLAM can work in an unknown statistical noise behavior and thus it is more robust. Experimental results demonstrate the effectiveness of the proposed algorithm.