期刊
DIGITAL COMMUNICATIONS AND NETWORKS
卷 7, 期 2, 页码 187-195出版社
KEAI PUBLISHING LTD
DOI: 10.1016/j.dcan.2020.08.002
关键词
Extended Kalman filter; Edge computing; Kalman filter; Localization; Robots; State estimation
资金
- Cross-Ministry Giga KOREA Project - Korea Government (MSIT) [GK20P0400]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [GK20P0400] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper introduces an EKF-based localization algorithm using edge computing to achieve higher accuracy and wider coverage. Simulation results show that the proposed algorithm is more accurate compared with current state-of-the-art localization algorithms.
The Extended Kalman Filter (EKF) has received abundant attention with the growing demands for robotic localization. The EKF algorithm is more realistic in non-linear systems, which has an autonomous white noise in both the system and the estimation model. Also, in the field of engineering, most systems are non-linear. Therefore, the EKF attracts more attention than the Kalman Filter (KF). In this paper, we propose an EKF-based localization algorithm by edge computing, and a mobile robot is used to update its location concerning the landmark. This localization algorithm aims to achieve a high level of accuracy and wider coverage. The proposed algorithm is helpful for the research related to the use of EKF localization algorithms. Simulation results demonstrate that, under the situations presented in the paper, the proposed localization algorithm is more accurate compared with the current state-of-the-art localization algorithms.
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