Lin Shanggang, Jin Lianwen, Chen Ziwei
School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China.
South China University of Technology-Zhuhai Institute of Modern Industrial Innovation, Zhuhai 519175, China.
Sensors (Basel). 2021 Sep 16;21(18):6226. doi: 10.3390/s21186226.
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.
让无人机自主且安全地着陆是一项具有挑战性的任务。尽管现有方法通过使用无人机的机载视觉系统识别特定着陆标记解决了精确着陆的问题，但这些工作绝大多数是在白天或光照良好的实验室环境中进行的。相比之下，很少有研究人员探讨通过使用各种有源光源照亮标记在低光照条件下着陆的可能性。在本文中，提出了一种新颖的视觉系统设计，以解决无人机在户外极低光照环境下的着陆问题，而无需对标记应用有源光源。我们使用基于模型的增强方案来提高机载捕获图像的质量和亮度，然后提出一种基于分层的方法，该方法由带有相关轻量级卷积神经网络（CNN）的决策树组成，用于从粗到精的着陆标记定位，其中标记的关键信息被提取并保留用于后处理，如姿态估计和着陆控制。已经进行了广泛的评估以证明所提出视觉系统的鲁棒性、准确性和实时性能。在标记位置平均亮度为5勒克斯的各种户外夜间场景中进行的现场实验证明了该系统的可行性和实用性。
