Unscented H∞ filter based simultaneous localization and mapping

Simultaneous localization and mapping (SLAM) is concerned to be the key point to realize the real autonomy of mobile robot. Kalman filter has been used as a popular solution by researchers in many SLAM applications. In order to avoid its shortcomings of assumption for Gaussian noises, this paper introduced unscented H∞ filter into SLAM problem. The proposed method requires no a priori knowledge of the noise statistics and relies only upon that the noise is bounded. Simulation results are presented to illustrate the effectiveness of the proposed method.

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