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论文类型：期刊论文

发表时间：2020-09-01

发表刊物：INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS

卷号：18

期号：9

页面范围：2315-2325

ISSN号：1598-6446

关键字：Adaptive factor; Masreliez-Martin unscented Kalman filter (MMUKF); robot tracking; simultaneous localization and mapping (SLAM)

摘要：The simultaneous localization and mapping (SLAM) is a significant topic in intelligent robot. In this paper, a robot tracking algorithm in SLAM with Masreliez-Martin unscented Kalman filter (MMUKF) is proposed. A robot dynamic model based on SLAM characteristics is first used as state equation to model the robotic movement, and the measurement equations are deduced by linearizing the motion model. Next, the covariance of process noise is estimated with an adaptive factor to improve tracking performance in the MMUKF. Finally, the MMUKF is employed to estimate the positions of robot and landmarks. The proposed algorithm can complete robot tracking with good accuracy, and obtain reliable state estimation in SLAM. Simulation results reveal the validity of the proposed algorithm.