Robust Visual Teach and Repeat Navigation for Unmanned Aerial Vehicles

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21730/21:00352831

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Robust Visual Teach and Repeat Navigation for Unmanned Aerial Vehicles

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Robust Visual Teach and Repeat Navigation for Unmanned Aerial Vehicles

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotika pro Průmysl 4.0</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 10th European Conference on Mobile Robots

ISBN

978-1-6654-1213-1

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Brussels

Místo konání akce

Bonn

Datum konání akce

31. 8. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—