Realtime Rooftop Landing Site Identification and Selection in Urban City Simulation
This addresses the need for safe autonomous landing in urban areas for the UAS industry, but is incremental as it builds on existing simulation and sensor fusion methods.
The paper tackles the problem of identifying safe landing zones for small unmanned aircraft systems on urban rooftops in real-time, using LiDAR and camera data, and presents a novel algorithm that fuses these data to find optimal obstacle-free landing positions, with results demonstrated in a high-fidelity simulated city environment.
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.