Abstract
Safe autonomous landing in urban cities is a necessity for the growing Unmanned Aircraft Systems (UAS) industry. In urgent situations, building rooftops, particularly flat rooftops, can provide local safe landing zones for small UAS. This paper investigates the real-time identification and selection of safe landing zones on rooftops based on LiDAR and camera sensor feedback. A visual high fidelity simulated city is constructed in the Unreal game engine, with particular attention paid to accurately generating rooftops and the common obstructions found thereon, e.g., ac units, water towers, air vents. AirSim, a robotic simulator plugin for Unreal, offers drone simulation and control and is capable of outputting video and LiDAR sensor data streams from the simulated Unreal world. A neural network is trained on randomized simulated cities to provide a pixel classification model. A novel algorithm is presented which finds the optimum obstacle-free landing position on nearby rooftops by fusing LiDAR and vision data.
Abstract (translated)
城市安全自主着陆是无人机系统(UAS)工业发展的必然要求。在紧急情况下,建筑屋顶,尤其是平屋顶,可以为小型无人机提供本地安全着陆区。本文研究了基于激光雷达和摄像机传感器反馈的屋顶安全着陆区的实时识别和选择。虚拟游戏引擎中构建了一个视觉高保真的模拟城市,特别注意准确生成屋顶和屋顶上常见的障碍物,如空调机组、水塔、通风口。Airsim是一个虚拟机器人模拟器插件,提供无人机模拟和控制,能够输出来自虚拟虚拟世界的视频和激光雷达传感器数据流。利用随机模拟城市训练神经网络,建立像素分类模型。提出了一种将激光雷达和视觉数据融合,在屋顶附近寻找最佳无障碍着陆位置的新算法。
URL
https://arxiv.org/abs/1903.03829