A Vision Based Forced Landing Site Selection System for an Autonomous UAV

This paper presents a system overview of the UAV forced landing site selection system and the results to date. The forced landing problem is a new field of research for UAVs and this paper will show the machine vision approach taken to address this problem. The results are based on aerial imagery collected from a series of flight trials in a Cessna 172. The aim of this research is to locate candidate landing sites for UAV forced landings, from aerial imagery. Output image frames highlight the algorithm's selected safe landing locations. The algorithms for the problem use image processing techniques and neural networks for the classification problem. The system is capable of locating areas that are large enough to land in and that are free of obstacles 92.3% ± 2% (95% confidence) of the time. These areas identified are then further classified as to their surface type to a classification accuracy of 90% ± 3% (98% confidence). It should be noted that although the system is being designed primarily for the forced landing problem for UAVs, the research can also be applied to forced landings or glider applications for piloted aircraft.