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Shuai Jia

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Proceedings Article | 13 December 2024 Paper

A dynamic initialization method for visual inertial odometry based on deep learning

Qinglin Liu, Shuai Jia, Hao Li, Yong Zhang

Proceedings Volume 13492, 1349205 (2024) https://doi.org/10.1117/12.3045786

KEYWORDS: Gyroscopes, Visualization, Deep learning, Navigation systems, Tunable filters, Matrices, Education and training, Angular velocity, Calibration

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Visual inertial odometry (VIO) requires accurate initial parameters before navigation, such as the system's initial pose, scale information, and inertial measurement unit (IMU) biases. Consequently, rapid and precise initialization is crucial for ensuring the smooth progress of subsequent navigation. However, when the system is in an environment lacking visual features, the system without measurement constraints will rapidly diverge. During this period, due to the unknown motion state, static initialization cannot be performed. To ensure that the system can navigate through this period as smoothly as possible and restart VIO, this paper proposes a dynamic initialization method for visual inertial odometry based on deep learning. This approach relies solely on inertial data, using a deep learning network to learn attitude errors and uncertainties, which are then utilized as measurement values and combined with an extended Kalman filter (EKF) to correct the system state. Experimental results on a public dataset show that the proposed method enables rapid dynamic initialization under short-term visual measurement absence, and effectively improves the system's attitude accuracy. Compared to the method of traditional inertial navigation solving after gyroscope calibration, the heading error of our proposed method is reduced by 13.38%.

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