  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.
