Guo, F., Yang, H., Wu, X., Dong, H., Wu, Q., & Li, Z. (2024). Model-Based Deep Learning for Low-Cost IMU Dead Reckoning of Wheeled Mobile Robot. IEEE Transactions on Industrial Electronics, 71(7), 7531–7541. https://doi.org/10.1109/tie.2023.3301531
Abstract:
Low-cost inertial measurement units (IMUs) suffer from low sensitivity and high random walk noise, which makes it challenging to use them directly for dead reckoning. Regular model-based inertial navigation methods require accurate modeling of IMU noise to get better results, while learning-based methods need plentiful datasets. In this paper, a novel low-cost IMU dead reckoning algorithm for wheeled mobile robot is introduced by integrating model-based and learning-based approaches, which inherits the merits of both methods. It achieves the dead-reckoning by using invariant extended Kalman filter (InEKF) and IMU error model, and computes the noise parameters of the model with the aid of a deep learning-based method. Our deep-learning based strategy is designed to obtain noise reduced inertial information of robots from low-cost IMU data such that the lnEKF can converge. The experimental results show that the proposed method can accurately estimate the attitude, velocity, and position of the wheeled mobile robot, and can compete with
vision algorithms. In addition, our proposed method consumes few computational resources to satisfy the needs of low-cost applications.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Robotics Horizontal Technology Coordinating Office (HTCO)
Grant Reference no. : C221518005
This work was supported in part by the National Natural Science
Foundation of China under Grants No. 62203391 and 62203390, in
part by the Natural Science Foundation of Zhejiang Province under
Grants No. LQ22F030015 and LDT23E05014F03, and in part by the Major Science and Technology Research Program of Jinhua under Grant No. 2021-1-012.