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Drone-based displacement measurement of infrastructures utilizing phase information

Author

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  • Shien Ri

    (National Institute of Advanced Industrial Science and Technology (AIST))

  • Jiaxing Ye

    (National Institute of Advanced Industrial Science and Technology (AIST))

  • Nobuyuki Toyama

    (National Institute of Advanced Industrial Science and Technology (AIST))

  • Norihiko Ogura

    (CORE Institute of Technology Corporation
    Kyoto University, Goryoohara, Nishikyo-Ku)

Abstract

Drone-based inspections provide an efficient and flexible approach to assessing aging infrastructures while prioritizing safety. Here, we present a pioneering framework that employs drone cameras for high-precision displacement measurement and achieves sub-millimeter accuracy, meeting the requirements for on-site inspections. Inspired by the principles of human auditory equilibrium, we have developed an effective scheme using a group of strategical reference markers on the bridge girders to measure structural displacements in the bridge. Our approach integrates the phase-based sampling moiré technique with four degrees-of-freedom geometric modeling to accurately delineate the desired bridge displacements from camera motion-induced displacements. The proposed scheme demonstrates favorable precision with accuracy reaching up to 1/100th of a pixel. Real-world validations further confirmed the reliability and efficiency of this technique, making it a practical tool for bridge displacement measurement. Beyond its current applications, this methodology holds promise as a foundational element in shaping the landscape of future autonomous infrastructure inspection systems.

Suggested Citation

  • Shien Ri & Jiaxing Ye & Nobuyuki Toyama & Norihiko Ogura, 2024. "Drone-based displacement measurement of infrastructures utilizing phase information," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44649-2
    DOI: 10.1038/s41467-023-44649-2
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    References listed on IDEAS

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    1. Artan Kaso, 2018. "Computation of the normalized cross-correlation by fast Fourier transform," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-16, September.
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