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：无人机（UAV）的显著增长也引发了对其任务期间安全措施的担忧。为推进更安全的自主空中机器人技术，本文提出了一种基于视觉的解决方案，以最少的基础设施保障自主无人机安全着陆。在对接机动过程中，无人机对附近人员构成危险。本文提出在着陆平台上使用单个朝上的全向全景相机，用于检测并估计着陆区域周围人员的位置。图像在嵌入式计算机中实时处理，该计算机与接近无人机的机载计算机通信，以在着陆、悬停或紧急着陆状态之间切换。着陆过程中，地面相机还辅助寻找最佳位置，这在电池电量低或无法继续悬停时可能必要。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，并使用开源ROS和PX4框架实现无人机的通信、接口与控制。通过仿真和真实室内实验结果展示了所提方法的有效性。