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arXiv · 2112.02143

CTIN: Robust Contextual Transformer Network for Inertial Navigation

Bingbing Rao·Ehsan Kazemi·Yifan Ding·Devu M Shila·Frank M. Tucker·Liqiang Wang
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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.

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Bingbing Rao, Ehsan Kazemi, Yifan Ding, Devu M Shila, Frank M. Tucker, Liqiang Wang. 2021-12-03. CTIN: Robust Contextual Transformer Network for Inertial Navigation. https://arxiv.org/abs/2112.02143

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