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Implementation of equipment maintenance and assembly assistance system based on augmented reality

Published: 31 December 2021 Publication History

Abstract

In this paper, the application of augmented reality technology aiming at the difficulty of parts assembly and disassembly in aviation equipment maintenance is studied. Considering parts recognition as one of the critical steps, the operations of image processing, feature extraction and feature matching are programmed, and the effects of SIFT and SURF feature extraction algorithms are compared. On this basis, two augmented reality tools, Vuforia and EasyAR, are used to build an assistance system for equipment maintenance and assembly. The user interface is designed to realize the display of image matching results and assembly tips. Through the practice above, the feasibility of intelligent maintenance technology using augmented reality technology is verified.

References

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  1. Implementation of equipment maintenance and assembly assistance system based on augmented reality

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    EITCE '21: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering
    October 2021
    1723 pages
    ISBN:9781450384322
    DOI:10.1145/3501409
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 31 December 2021

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    Author Tags

    1. assembly assistance
    2. augmented reality
    3. computer vision
    4. image processing
    5. target recognition

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    EITCE 2021

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    EITCE '21 Paper Acceptance Rate 294 of 531 submissions, 55%;
    Overall Acceptance Rate 508 of 972 submissions, 52%

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