CTIN: Robust Contextual Transformer Network for Inertial Navigation

• URL: http://arxiv.org/abs/2112.02143v1
• Date: Fri, 3 Dec 2021 19:57:34 GMT
• Title: CTIN: Robust Contextual Transformer Network for Inertial Navigation
• Authors: Bingbing Rao, Ehsan Kazemi, Yifan Ding, Devu M Shila, Frank M. Tucker, Liqiang Wang
• Abstract summary: We propose a robust Con Transformer-based network for Inertial Navigation(CTIN) to accurately predict velocity and trajectory. CTIN is very robust and outperforms state-of-the-art models.
• Score: 20.86392550313961
• License: http://creativecommons.org/licenses/by/4.0/
• 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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