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MSIN: An Efficient Multi-head Self-attention Framework for Inertial Navigation

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Algorithms and Architectures for Parallel Processing (ICA3PP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14487))

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Abstract

Inertial Measurement Unit (IMU) makes an outstanding contribution to indoor inertial navigation in the era of ubiquitous computing, as it is widely integrated into portable devices. Many prominent works have been proposed by taking gyroscope and accelerometer readings as input to estimate the velocity and orientation. However, most of them focus on the local features of IMU (i.e., single sensor temporal feature or local spatial feature), eventually leading to drift on the trajectory. In this paper, we propose a robust model to mitigate the problem of jitters and drifts in trajectory prediction by exploiting the spatial dependence in accelerometer and gyroscope readings, as well as the contextual relation in motion terms through in-depth analyses of IMU readings. In particular, we design a framework (MSIN) to fuse the local spatial dependence of multiple sensors and incorporate the local spatial and global temporal features by using the multi-head self-attention mechanism. We have conducted extensive experiments on two public datasets and the results show that MSIN achieves a significant improvement in RTE (Relative Trajectory Error) performance, with improvements of up to 6.14% and 15.19% over state-of-the-art methods for RoNIN-Unseen and RIDI-Unseen, respectively.

This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61872266.

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Author information

Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Gaotao Shi & Bingjia Pan

  2. Tianjin Key Laboratory of Advanced Networking (TANK), Tianjin, China

    Gaotao Shi & Bingjia Pan

  3. Tianjin International Engineering Institute, Tianjin University, Tianjin, China

    Yuzhi Ni

Authors
  1. Gaotao Shi
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  2. Bingjia Pan
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  3. Yuzhi Ni
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Correspondence to Gaotao Shi .

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Editors and Affiliations

  1. Royal Melbourne Institute of Technology, Melbourne, VIC, Australia

    Zahir Tari

  2. Tianjin University, Tianjin, China

    Keqiu Li

  3. University of Arizona, Tucson, AZ, USA

    Hongyi Wu

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Shi, G., Pan, B., Ni, Y. (2024). MSIN: An Efficient Multi-head Self-attention Framework for Inertial Navigation. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14487. Springer, Singapore. https://doi.org/10.1007/978-981-97-0834-5_26

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  • DOI: https://doi.org/10.1007/978-981-97-0834-5_26

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  • Print ISBN: 978-981-97-0833-8

  • Online ISBN: 978-981-97-0834-5

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