Precise Localization of a UAV with Single Vision Camera and Deep Learning

Hyeong Tae Kim, Hwangnam Kim

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    This paper suggests a novel method of detecting and estimating the position of Unmanned Aerial Vehicle (UAV) with a single monocular camera only. As the leverage of UAV is keep on increasing, the related research has been extremely developed. To successfully use a UAV in a variety of missions, a precise localization technique is essential. However, there is still a lack of research to accurately measure the vehicle's present altitude. Thus, this study conducted a simple but accurate altitude measurement method using a camera. First, UAV detection is initially proceeded by using a deep learning approach. After determining that the object displayed in the image is UAV, the altitude is calculated with a distance measuring formula using the camera's Field of View (FOV). Besides, zooming, cropping, and some image processing are performed to enhance the accuracy of the altitude value. As a result, average errors of less than 5% and errors of up to 60cm were obtained, which is an improvement over previous altitude measurement techniques. This method can calibrate the altitude of the UAV immediately in a relatively inexpensive and simple way.

    Original languageEnglish
    Title of host publication2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728182988
    DOIs
    Publication statusPublished - 2020 Dec
    Event2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China
    Duration: 2020 Dec 72020 Dec 11

    Publication series

    Name2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings

    Conference

    Conference2020 IEEE Global Communications Conference, GLOBECOM 2020
    Country/TerritoryTaiwan, Province of China
    CityVirtual, Taipei
    Period20/12/720/12/11

    Bibliographical note

    Funding Information:
    ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2020R1A2C1012389).

    Publisher Copyright:
    © 2020 IEEE.

    Keywords

    • Altitude
    • Deep learning
    • FOV
    • Localization
    • UAV

    ASJC Scopus subject areas

    • Media Technology
    • Modelling and Simulation
    • Instrumentation
    • Artificial Intelligence
    • Computer Networks and Communications
    • Hardware and Architecture
    • Software
    • Safety, Risk, Reliability and Quality

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