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Robust Visual Teach and Repeat Navigation for Unmanned Aerial Vehicles

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00352831" target="_blank" >RIV/68407700:21230/21:00352831 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21730/21:00352831

  • Result on the web

    <a href="https://doi.org/10.1109/ECMR50962.2021.9568807" target="_blank" >https://doi.org/10.1109/ECMR50962.2021.9568807</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ECMR50962.2021.9568807" target="_blank" >10.1109/ECMR50962.2021.9568807</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Robust Visual Teach and Repeat Navigation for Unmanned Aerial Vehicles

  • Original language description

    Vision-based navigation is one of the leading tasks in mobile robotics. It, however, introduces additional challenges in long-term autonomy due to its reliance on stable visual features. As such, visual navigation methods are often sensitive to appearance changes and unreliable in environments with low feature density. We present a teach-and-repeat navigation system for unmanned aerial vehicles (UAVs) equipped with a low-end camera. We use a novel visual place recognition methodology based on high-level CNN features to localize a robot on a previously traversed trajectory and to directly calculate heading corrections for navigation. The developed navigation method is fully vision-based and independent of other sensory information, making it universal and easily transferable. The system has been experimentally verified and evaluated with respect to a state-of-the-art ORB2-TaR navigation system. It showed comparable results in terms of its precision and robustness to environmental changes. In addition, the system was able to safely navigate in environments with low feature density and to reliably solve the wake-up robot problem.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotics 4 Industry 4.0</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 10th European Conference on Mobile Robots

  • ISBN

    978-1-6654-1213-1

  • ISSN

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

  • Publisher name

    IEEE

  • Place of publication

    Brussels

  • Event location

    Bonn

  • Event date

    Aug 31, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article