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- Title
Robust SLAM localization method based on improved variational Bayesian filtering.
- Authors
Zhai Hongqi; Wang Lihui; Cai Tijing; Meng Qian
- 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.
- Subjects
SLAM (Robotics); KALMAN filtering; SAMPLING errors; ALGORITHMS; ARTIFICIAL neural networks
- Publication
Journal of Southeast University (English Edition), 2022, Vol 38, Issue 4, p340
- ISSN
1003-7985
- Publication type
Article
- DOI
10.3969/j.issn.1003-7985.2022.04.003