Realtime Rooftop Landing Site Identification and Selection in Urban City Simulation

TL;DR

This paper presents a real-time system for identifying and selecting safe rooftop landing sites for UAS in urban environments using LiDAR and camera data within a high-fidelity simulation.

Contribution

It introduces a novel algorithm that fuses LiDAR and vision data to find obstacle-free landing zones on rooftops in real-time, validated in a simulated urban environment.

Findings

Effective identification of safe landing zones in simulation

Fusion of LiDAR and vision improves accuracy

Real-time processing enables practical deployment

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.