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.