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Dear Colleagues,

Recently, the unmanned aerial vehicle (UAV) industry has been experiencing rapid growth, and UAVs are starting to play an important role in various missions such as search and rescue, environmental monitoring, security monitoring, transportation, and inspection. A lot of research into various aspects regarding sensor-based navigation for UAVs and various robots is being actively conducted in academia or by research institutes. This is because state estimation or active searches in an unknown environment using sensor information is essential indoors or in areas where GPS is not available.

This Special Issue aims to contribute to an increase in the level of knowledge in the context of vision-based UAV navigation. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Visual–inertial odometry/visual SLAM on UAVs;
Various optic-sensor-based technologies (event camera, thermal camera, etc.);
Multi-robot collaborative V-SLAM for autonomous navigation;
Deep-learning-based navigation;
Sensor fusion (vision, LiDAR, UWB, etc.);
Applications in real fields (subterranean, warehouses, etc.).

Dr. Sungwook Jung
Guest Editor

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Keywords

visual–inertial odometry
visual SLAM
visual navigation for UAVs
multi-robot navigation
multi-sensor fusion
state estimation

Published Papers (1 paper)

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27 pages, 3739 KiB

Open AccessArticle

Visual Navigation Algorithm for Night Landing of Fixed-Wing Unmanned Aerial Vehicle

by Zhaoyang Wang, Dan Zhao and Yunfeng Cao

Aerospace 2022, 9(10), 615; https://doi.org/10.3390/aerospace9100615 - 17 Oct 2022

Cited by 5 | Viewed by 1901

Abstract

In the recent years, visual navigation has been considered an effective mechanism for achieving an autonomous landing of Unmanned Aerial Vehicles (UAVs). Nevertheless, with the limitations of visual cameras, the effectiveness of visual algorithms is significantly limited by lighting conditions. Therefore, a novel vision-based autonomous landing navigation scheme is proposed for night-time autonomous landing of fixed-wing UAV. Firstly, due to the difficulty of detecting the runway caused by the low-light image, a strategy of visible and infrared image fusion is adopted. The objective functions of the fused and visible image, and the fused and infrared image, are established. Then, the fusion problem is transformed into the optimal situation of the objective function, and the optimal solution is realized by gradient descent schemes to obtain the fused image. Secondly, to improve the performance of detecting the runway from the enhanced image, a runway detection algorithm based on an improved Faster region-based convolutional neural network (Faster R-CNN) is proposed. The runway ground-truth box of the dataset is statistically analyzed, and the size and number of anchors in line with the runway detection background are redesigned based on the analysis results. Finally, a relative attitude and position estimation method for the UAV with respect to the landing runway is proposed. New coordinate reference systems are established, six landing parameters, such as three attitude and three positions, are further calculated by Orthogonal Iteration (OI). Simulation results reveal that the proposed algorithm can achieve 1.85% improvement of AP on runway detection, and the reprojection error of rotation and translation for pose estimation are

0 . 675^{\circ}

and 0.581%, respectively. Full article

(This article belongs to the Special Issue Vision-Based UAV Navigation)

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