A velocity estimation network based on inertial measurement unit array

Bing Yang; Jiao Lv; Fengrong Huang

doi:10.1117/12.3039336

4 September 2024 A velocity estimation network based on inertial measurement unit array

Bing Yang, Jiao Lv, Fengrong Huang

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132591Y (2024) https://doi.org/10.1117/12.3039336
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

With the advancement of autonomous driving and robotic technologies, the demand for precise navigation and localization is increasing. To address the problem of error accumulation in inertial navigation and the challenges in mining data from inertial measurement units (IMUs), this paper proposes a velocity estimation network based on IMU arrays. This network integrates methods from self-supervised and supervised learning to enhance the accuracy and robustness of velocity estimation. We designed a network structure comprising an encoder and a velocity decoder. The encoder utilizes an improved BERT model for self-supervised pre-training, employing masking reconstruction techniques to effectively extract features from IMU data. The velocity decoder adopts a multilayer perceptron (MLP) architecture to further integrate features for accurate velocity estimation. Experimental results validate the effectiveness of the proposed method, demonstrating that it can effectively utilize data from IMU arrays to achieve accurate velocity estimates.

Citation Download Citation

Bing Yang, Jiao Lv, and Fengrong Huang "A velocity estimation network based on inertial measurement unit array", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132591Y (4 September 2024); https://doi.org/10.1117/12.3039336

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