A multi-innovation with forgetting factor based EKF-SLAM method for mobile robots
ISSN: 0144-5154
Article publication date: 28 December 2020
Issue publication date: 19 February 2021
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