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$H_{\infty}$ Filter Based Robust Simultaneous Localization and Mapping for Mobile Robots  

Jeon, Seo-Hyun (ETRI)
Lee, Keon-Yong (Korea University)
Doh, Nakju Lett (Korea University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.48, no.1, 2011 , pp. 55-60 More about this Journal
Abstract
The most basic algorithm in SLAM(Simultaneous Localization And Mapping) technique of mobile robots is EKF(Extended Kalman Filter) SLAM. However, it requires prior information of characteristics of the system and the noise model which cannot be estimated in accurate. By this limit, Kalman Filter shows the following behaviors in a highly uncertain environment: becomes too sensitive to internal parameters, mathematical consistency is not kept, or yields a wrong estimation result. In contrast, $H_{\infty}$ filter does not requires a prior information in detail. Thus, based on a idea that $H_{\infty}$ filter based SLAM will be more robust than the EKF-SLAM, we propose a framework of $H_{\infty}$ filter based SLAM and show that suggested algorithm shows slightly better result man me EKF-SLAM in a highly uncertain environment.
Keywords
$H_{\infty}$ filter; Kalman Filter; EKF-SLAM; mobile robot;
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