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A multi-innovation with forgetting factor based EKF-SLAM method for mobile robots

Zhen Zhou (School of Automation, Qingdao University, Qingdao, China)
Dongqing Wang (College of Electrical Engineering, Qingdao University, Qingdao, China)
Boyang Xu (School of Automation, Qingdao University, Qingdao, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 28 December 2020

Issue publication date: 19 February 2021

293

Abstract

Purpose

The purpose of this paper is to explore a multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm to solve the error increasing problem, caused by the Extended Kalman filtering (EKF) violating the local linear assumption in simultaneous localization and mapping (SLAM) for mobile robots because of strong nonlinearity.

Design/methodology/approach

A multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm is investigated. At each filtering step, the FMI-EKF-SLAM algorithm expands the single innovation at current step to an extended multi-innovation containing current and previous steps and introduces the forgetting factor to reduce the effect of old innovations.

Findings

The simulation results show that the explored FMI-EKF-SLAM method reduces the state estimation errors, obtains the ideal filtering effect and achieves higher accuracy in positioning and mapping.

Originality/value

The method proposed in this paper improves the positioning accuracy of SLAM and improves the EKF, so that the EKF has higher accuracy and wider application range.

Keywords

Acknowledgements

Funding: This study was funded by the National Natural Science Foundation of China (Grant No. 61873138).,and in part by the Taishan Scholar Project Fund of Shandong Province of China.

Conflict of Interest: The authors declare that they have no conflict of interest.

Citation

Zhou, Z., Wang, D. and Xu, B. (2021), "A multi-innovation with forgetting factor based EKF-SLAM method for mobile robots", Assembly Automation, Vol. 41 No. 1, pp. 71-78. https://doi.org/10.1108/AA-01-2020-0002

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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