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Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

Landing an unmanned aerial vehicle (UAV) autonomously and safely is a challenging task. Although the existing approaches have resolved the problem of precise landing by identifying a specific landing marker using the UAV’s onboard vision system, the vast majority of these works are conducted in eith...

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Autores principales: Lin, Shanggang, Jin, Lianwen, Chen, Ziwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471562/
https://www.ncbi.nlm.nih.gov/pubmed/34577433
http://dx.doi.org/10.3390/s21186226
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author Lin, Shanggang
Jin, Lianwen
Chen, Ziwei
author_facet Lin, Shanggang
Jin, Lianwen
Chen, Ziwei
author_sort Lin, Shanggang
collection PubMed
description Landing an unmanned aerial vehicle (UAV) autonomously and safely is a challenging task. Although the existing approaches have resolved the problem of precise landing by identifying a specific landing marker using the UAV’s onboard vision system, the vast majority of these works are conducted in either daytime or well-illuminated laboratory environments. In contrast, very few researchers have investigated the possibility of landing in low-illumination conditions by employing various active light sources to lighten the markers. In this paper, a novel vision system design is proposed to tackle UAV landing in outdoor extreme low-illumination environments without the need to apply an active light source to the marker. We use a model-based enhancement scheme to improve the quality and brightness of the onboard captured images, then present a hierarchical-based method consisting of a decision tree with an associated light-weight convolutional neural network (CNN) for coarse-to-fine landing marker localization, where the key information of the marker is extracted and reserved for post-processing, such as pose estimation and landing control. Extensive evaluations have been conducted to demonstrate the robustness, accuracy, and real-time performance of the proposed vision system. Field experiments across a variety of outdoor nighttime scenarios with an average luminance of 5 lx at the marker locations have proven the feasibility and practicability of the system.
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spelling pubmed-84715622021-09-28 Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments Lin, Shanggang Jin, Lianwen Chen, Ziwei Sensors (Basel) Article Landing an unmanned aerial vehicle (UAV) autonomously and safely is a challenging task. Although the existing approaches have resolved the problem of precise landing by identifying a specific landing marker using the UAV’s onboard vision system, the vast majority of these works are conducted in either daytime or well-illuminated laboratory environments. In contrast, very few researchers have investigated the possibility of landing in low-illumination conditions by employing various active light sources to lighten the markers. In this paper, a novel vision system design is proposed to tackle UAV landing in outdoor extreme low-illumination environments without the need to apply an active light source to the marker. We use a model-based enhancement scheme to improve the quality and brightness of the onboard captured images, then present a hierarchical-based method consisting of a decision tree with an associated light-weight convolutional neural network (CNN) for coarse-to-fine landing marker localization, where the key information of the marker is extracted and reserved for post-processing, such as pose estimation and landing control. Extensive evaluations have been conducted to demonstrate the robustness, accuracy, and real-time performance of the proposed vision system. Field experiments across a variety of outdoor nighttime scenarios with an average luminance of 5 lx at the marker locations have proven the feasibility and practicability of the system. MDPI 2021-09-16 /pmc/articles/PMC8471562/ /pubmed/34577433 http://dx.doi.org/10.3390/s21186226 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lin, Shanggang
Jin, Lianwen
Chen, Ziwei
Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments
title Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments
title_full Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments
title_fullStr Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments
title_full_unstemmed Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments
title_short Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments
title_sort real-time monocular vision system for uav autonomous landing in outdoor low-illumination environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471562/
https://www.ncbi.nlm.nih.gov/pubmed/34577433
http://dx.doi.org/10.3390/s21186226
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