IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-032-04458-7_24.html

LiDAR and InSAR Signals for UAV Autonomous Navigation Over an Amazon Region

Author

Listed:
  • Roberto N. Salles

    (IEAv: Instituto de Estudos Avanãados)

  • Haroldo F. de Campos Velho

    (INPE: Instituto Nacional de Pesquisas Espaciais)

  • Elcio H. Shiguemori

    (IEAv: Instituto de Estudos Avanãados
    INPE: Instituto Nacional de Pesquisas Espaciais)

Abstract

This work addresses the geo-localization problem of an Unmanned Aerial Vehicle (UAV), or aerial drone, flying over a densely forested region in the Brazilian Amazon using Computer Vision. LiDAR cloud point data were post-processed to become 2D template windows, and by applying the template matching algorithm against a geo-referenced elevation model from the interferometric synthetic aperture radar (InSAR) satellite data, positions were estimated. The template matching used the Normalized Cross-correlation method. The LiDAR 3D cloud was processed through binning to become 2D 12.5 m pixel spacing surface images, compatible with the ALOS terrain reference map. This application of template matching achieved 48.5 m root mean square error, or less than four pixels, over a simulated route. Position estimation is essential for automatic navigation of aerial vehicles, and experiments with LiDAR data show potential for localization over densely forested regions, where methods using optical camera data may fail to acquire distinguishable features.

Suggested Citation

  • Roberto N. Salles & Haroldo F. de Campos Velho & Elcio H. Shiguemori, 2026. "LiDAR and InSAR Signals for UAV Autonomous Navigation Over an Amazon Region," Springer Books,, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-04458-7_24
    DOI: 10.1007/978-3-032-04458-7_24
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:sprchp:978-3-032-04458-7_24. 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.springer.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.