Robot Simultaneous Localization and Mapping AlgorithmBased on Improved Fuzzy Adaptive Kalman Filter

doi:10.3969/j.issn.1006-2475.2014.03.002

Abstract

Abstract: Robot simultaneous localization and mapping (SLAM) problem is a very important issue in the robotic field. The main tasks of SLAM include how to reduce the localization error, improve the robustness of the algorithms effectively and improve the accuracy of robot simultaneous localization and mapping. Aim at this problem, an improved fuzzy adaptive extended Kalman filter (EKF) is proposed. In the proposed approach, a fuzzy adaptive control model is used to adjust the system noise and observation noise. Finally, some simulation experiments are conducted, and the experimental results show that the proposed approach can solve the divergence of Kalman filter and reduce the robot localization error effectively.

Key words: SLAM problem, localization error, fuzzy adaptive control model, robot

CLC Number:

TP301.6

WANG Chu, NI Jian-jun, YIN Xia-hong. Robot Simultaneous Localization and Mapping AlgorithmBased on Improved Fuzzy Adaptive Kalman Filter[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2014.03.002.

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