The H<inf>∞</inf> FastSLAM framework

Abstract

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 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> and R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> ) that are in most applications unknown. On the other hand, an incorrect a priori knowledge of Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> and R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> may seriously degrade the performance of FastSLAM. To solve these problems, this paper presents H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> FastSLAM. In this approach, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> particle filter is used for the mobile robot position estimation and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter is used for the feature location's estimation. The H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> FastSLAM can work in an unknown statistical noise behavior and thus it is more robust. Experimental results demonstrate the effectiveness of the proposed algorithm.