|Table of Contents|

[1] Zhai Hongqi, Wang Lihui, Cai Tijing, Meng Qian, et al. Robust SLAM localization methodbased on improved variational Bayesian filtering [J]. Journal of Southeast University (English Edition), 2022, 38 (4): 340-349. [doi:10.3969/j.issn.1003-7985.2022.04.003]
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Robust SLAM localization methodbased on improved variational Bayesian filtering()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
38
Issue:
2022 4
Page:
340-349
Research Field:
Automation
Publishing date:
2022-12-20

Info

Title:
Robust SLAM localization methodbased on improved variational Bayesian filtering
Author(s):
Zhai Hongqi Wang Lihui Cai Tijing Meng Qian
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology of Ministry of Education, Southeast University, Nanjing 210096, China
Keywords:
underwater navigation and positioning non-Gaussian distribution time-varying noise variational Bayesian method simultaneous localization and mapping(SLAM)
PACS:
TP242.6
DOI:
10.3969/j.issn.1003-7985.2022.04.003
Abstract:
Aimed at the problem that the state estimation in the measurement update of the simultaneous localization and mapping(SLAM)method is incorrect or even not convergent because of the non-Gaussian measurement noise, outliers, or unknown and time-varying noise statistical characteristics, a robust SLAM method based on the improved variational Bayesian adaptive Kalman filtering(IVBAKF)is proposed. First, the measurement noise covariance is estimated using the variable Bayesian adaptive filtering algorithm. Then, the estimated covariance matrix is robustly processed through the weight function constructed in the form of a reweighted average. Finally, the system updates are iterated multiple times to further gradually correct the state estimation error. Furthermore, to observe features at different depths, a feature measurement model containing depth parameters is constructed. Experimental results show that when the measurement noise does not obey the Gaussian distribution and there are outliers in the measurement information, compared with the variational Bayesian adaptive SLAM method, the positioning accuracy of the proposed method is improved by 17.23%, 20.46%, and 17.76%, which has better applicability and robustness to environmental disturbance.

References:

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Memo

Memo:
Biographies: Zhai Hongqi(1990—), female, Ph.D. graduate; Wang Lihui(corresponding author), male, doctor, professor, wlhseu@163.com.
Foundation items: Primary Research and Development Plan of Jiangsu Province(No. BE2022389), Jiangsu Province Agricultural Science and Technology Independent Innovation Fund Project(No.CX(22)3091), the National Natural Science Foundation of China(No. 61773113).
Citation: Zhai Hongqi, Wang Lihui, Cai Tijing, et al. Robust SLAM localization method based on improved variational Bayesian filtering[J].Journal of Southeast University(English Edition), 2022, 38(4):340-349.DOI:10.3969/j.issn.1003-7985.2022.04.003.
Last Update: 2022-12-20