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CTIN: Robust Contextual Transformer Network for Inertial Navigation

2021-12-03 19:57:34
Bingbing Rao, Ehsan Kazemi, Yifan Ding, Devu M Shila, Frank M. Tucker, Liqiang Wang

Abstract

Recently, data-driven inertial navigation approaches have demonstrated their capability of using well-trained neural networks to obtain accurate position estimates from inertial measurement units (IMU) measurements. In this paper, we propose a novel robust Contextual Transformer-based network for Inertial Navigation~(CTIN) to accurately predict velocity and trajectory. To this end, we first design a ResNet-based encoder enhanced by local and global multi-head self-attention to capture spatial contextual information from IMU measurements. Then we fuse these spatial representations with temporal knowledge by leveraging multi-head attention in the Transformer decoder. Finally, multi-task learning with uncertainty reduction is leveraged to improve learning efficiency and prediction accuracy of velocity and trajectory. Through extensive experiments over a wide range of inertial datasets~(e.g. RIDI, OxIOD, RoNIN, IDOL, and our own), CTIN is very robust and outperforms state-of-the-art models.

Abstract (translated)

URL

https://arxiv.org/abs/2112.02143

PDF

https://arxiv.org/pdf/2112.02143.pdf


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