The remarkable growth of unmanned aerial vehicles (UAVs) has also sparked concerns about safety measures during their missions. To advance towards safer autonomous aerial robots, this work presents a vision-based solution to ensuring safe autonomous UAV landings with minimal infrastructure. During docking maneuvers, UAVs pose a hazard to people in the vicinity. In this paper, we propose the use of a single omnidirectional panoramic camera pointing upwards from a landing pad to detect and estimate the position of people around the landing area. The images are processed in real-time in an embedded computer, which communicates with the onboard computer of approaching UAVs to transition between landing, hovering or emergency landing states. While landing, the ground camera also aids in finding an optimal position, which can be required in case of low-battery or when hovering is no longer possible. We use a YOLOv7-based object detection model and a XGBooxt model for localizing nearby people, and the open-source ROS and PX4 frameworks for communication, interfacing, and control of the UAV. We present both simulation and real-world indoor experimental results to show the efficiency of our methods.

翻译：无人飞行器（UAV）的迅猛发展也引发了对其任务过程中安全措施的担忧。为推进更安全的自主空中机器人技术，本文提出一种基于视觉的解决方案，以最少的基础设施投入确保无人机自主着陆的安全性。在对接操作中，无人机对附近人员构成潜在危险。本文提出使用着陆平台上方向上的单台全景相机，检测并估算着陆区域周围人员的位置。嵌入式计算机实时处理图像，并与接近无人机的机载计算机通信，据此在着陆、悬停或紧急着陆状态间切换。着陆过程中，地面相机还有助于寻找最优位置，这在电池电量不足或无法继续悬停时尤为必要。我们采用基于YOLOv7的目标检测模型和XGBoost模型对附近人员进行定位，并利用开源ROS和PX4框架实现无人机的通信、接口与控制。通过仿真和室内真实环境实验，验证了方法的有效性。