IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-62801-y.html
   My bibliography  Save this article

Slope inspection under dense vegetation using LiDAR-based quadrotors

Author

Listed:
  • Wenyi Liu

    (The University of Hong Kong)

  • Yunfan Ren

    (The University of Hong Kong)

  • Rui Guo

    (The University of Hong Kong)

  • Vickie W. W. Kong

    (The Government of Hong Kong SAR)

  • Anthony S. P. Hung

    (The Government of Hong Kong SAR)

  • Fangcheng Zhu

    (The University of Hong Kong)

  • Yixi Cai

    (The University of Hong Kong)

  • Huajie Wu

    (The University of Hong Kong)

  • Yuying Zou

    (The University of Hong Kong)

  • Fu Zhang

    (The University of Hong Kong)

Abstract

Landslides pose significant threats to residents’ safety and daily lives. To mitigate such risks, flexible debris-resisting barriers are constructed and regularly inspected, a task known as slope inspection. Traditional manual inspections are costly and difficult due to steep terrains and dense vegetation. Unmanned aerial vehicle (UAV) equipped with LiDAR and cameras offers high mobility, making them well-suited for slope inspections. However, existing UAV solutions lack comprehensive frameworks to handle dense vegetation, including robust localization, high-precision mapping, small and dynamic obstacle avoidance, and cluttered under-canopy navigation. To address these challenges, we develop a LiDAR-based quadrotor with a comprehensive software system. Our quadrotor features assisted obstacle avoidance, enabling it to autonomously avoid intricate obstacles while executing pilot commands. Field experiments conducted in collaboration with the Hong Kong Civil Engineering and Development Department demonstrate our quadrotor’s ability to avoid small obstacles and maneuver in dense vegetation, validating its practical potential for slope inspection.

Suggested Citation

  • Wenyi Liu & Yunfan Ren & Rui Guo & Vickie W. W. Kong & Anthony S. P. Hung & Fangcheng Zhu & Yixi Cai & Huajie Wu & Yuying Zou & Fu Zhang, 2025. "Slope inspection under dense vegetation using LiDAR-based quadrotors," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62801-y
    DOI: 10.1038/s41467-025-62801-y
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-62801-y
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-025-62801-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62801-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.