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论文类型：期刊论文

发表时间：2020-12-01

发表刊物：ROBOTICS AND AUTONOMOUS SYSTEMS

卷号：134

ISSN号：0921-8890

关键字：Simultaneous localization and mapping (SLAM); FastSLAM; Unscented Kalman filter; Particle filter; Time varying noise estimator; Adaptive genetic algorithm

摘要：The FastSLAM is a typical tracking algorithm for SLAM, but it often suffers from the low tracking accuracy. To mitigate the problem, an improved H-Infinity unscented FastSLAM (IHUFastSLAM) with adaptive genetic resampling is proposed in this paper. Specifically, the H-Infinity unscented Kalman filter algorithm is improved using an adaptive factor and is employed as importance sampling in particle filter. Next, the process noise and the measurement noise are estimated by a time varying noise estimator. Moreover, an adaptive genetic algorithm is used to complete the resampling of particle filter. Finally, the improved H-Infinity UFastSLAM with adaptive genetic resampling is proposed to complete robot tracking. The proposed algorithm can track robot with good accuracy, and obtain reliable state estimation in SLAM. Simulation results reveal the validity of the proposed algorithm. (C) 2020 Elsevier B.V. All rights reserved.