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Title: A robust extended H∞ filtering approach to multi-robot cooperative localization in dynamic indoor environments
Other Titles: A robust extended H-infinity filtering approach to multi-robot cooperative localization in dynamic indoor environments
Authors: Zhuang, Y
Wang, Z
Yu, H
Wang, W
Lauria, S
Keywords: multi-robot cooperative localization;robust extended H∞ filtering (REHF);metric-based Iterative Closest Point (MbICP);laser data interaction
Issue Date: 17-Apr-2013
Publisher: Elsevier
Citation: Control Engineering Practice, 21(7): 953 - 961, Jul 2013
Abstract: Multi-robot cooperative localization serves as an essential task for a team of mobile robots to work within an unknown environment. Based on the real-time laser scanning data interaction, a robust approach is proposed to obtain optimal multi-robot relative observations using the Metric-based Iterative Closest Point (MbICP) algorithm, which makes it possible to utilize the surrounding environment information directly instead of placing a localization-mark on the robots. To meet the demand of dealing with the inherent non-linearities existing in the multi-robot kinematic models and the relative observations, a robust extended H∞ filtering (REHF) approach is developed for the multi-robot cooperative localization system, which could handle non-Gaussian process and measurement noises with respect to robot navigation in unknown dynamic scenes. Compared with the conventional multi-robot localization system using extended Kalman filtering (EKF) approach, the proposed filtering algorithm is capable of providing superior performance in a dynamic indoor environment with outlier disturbances. Both numerical experiments and experiments conducted for the Pioneer3-DX robots show that the proposed localization scheme is effective in improving both the accuracy and reliability of the performance within a complex environment.
ISSN: 0967-0661
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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