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.

中文翻译：

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用于室外低照度环境下无人机自主着陆的实时单目视觉系统

自主安全地降落无人驾驶飞行器 (UAV) 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来解决精确着陆的问题，但这些工作中的绝大多数是在白天或光线充足的实验室环境中进行的。相比之下，很少有研究人员通过使用各种有源光源来照亮标记来研究在低照度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来解决无人机在室外极低照度环境中的着陆问题，而无需将有源光源应用于标记。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，然后提出一种基于层次的方法，由决策树和相关联的轻量级卷积神经网络 (CNN) 组成，用于从粗到细的着陆标记定位，其中提取标记的关键信息并保留用于后处理，例如姿态估计和着陆控制。已经进行了广泛的评估，以证明所提出的视觉系统的鲁棒性、准确性和实时性能。在标记位置平均亮度为 5 lx 的各种户外夜间场景的现场实验证明了该系统的可行性和实用性。其中提取标记的关键信息并保留用于后处理，例如姿态估计和着陆控制。已经进行了广泛的评估，以证明所提出的视觉系统的鲁棒性、准确性和实时性能。在标记位置平均亮度为 5 lx 的各种户外夜间场景的现场实验证明了该系统的可行性和实用性。其中提取标记的关键信息并保留用于后处理，例如姿态估计和着陆控制。已经进行了广泛的评估，以证明所提出的视觉系统的鲁棒性、准确性和实时性能。在标记位置平均亮度为 5 lx 的各种户外夜间场景的现场实验证明了该系统的可行性和实用性。