Multi-model state estimation method for monocular visual-inertial systems in autonomous driving

Shen Ma; Jianzhong Li; Zhiyi Han; Xiaohui Qin

doi:10.1117/12.2641932

10 November 2022 Multi-model state estimation method for monocular visual-inertial systems in autonomous driving

Shen Ma, Jianzhong Li, Zhiyi Han, Xiaohui Qin

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123480D (2022) https://doi.org/10.1117/12.2641932
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

Monocular visual-inertial system with high accuracy and robustness is a popular choice for vehicle localization. The system incorporates data of camera and IMU to obtain accurate poses through state estimation of the back-end module. However, degenerate motions of vehicle usually make states unobservable, which then increases the errors of state estimation. In this paper, we propose a multi-model state estimation method to avoid this situation. Three kinds of modes are identified according to the observability of the system in various motions. We weight the error of scale and gyroscope bias to select mode in mode judgment module. Additionally, we combine PnP and the integration of IMU data to accurately obtain the new frame’s state. To achieve the optimal estimation, we abandon to optimize unobservable states and keep them in initial values. Our method is verified on real-world experiments and gets better performance than traditional state estimate methods.

Citation Download Citation

Shen Ma, Jianzhong Li, Zhiyi Han, and Xiaohui Qin "Multi-model state estimation method for monocular visual-inertial systems in autonomous driving", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123480D (10 November 2022); https://doi.org/10.1117/12.2641932

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