LiDAR-Aided Relative and Absolute Localization for Automated UAV-based Inspection of Aircraft Fuselages

Robust localization is a crucial task for various robotic applications, such as the inspection of aircraft surfaces by autonomous Unmanned Aerial Vehicles (UAV). Currently, aircraft inspection in mostly done in GNSS-denied hangar environments but UAV indoor localization using external positioning systems has several deficits regarding practicability, safety and security. Since aircraft standing times are the crucial cost factor, inspection should perspectively be shifted outdoors to the runway in order to further increase efficiency, which requires an onboard localization of the UAV relative to the aircraft that ensures robustness by working independendly of hangar features or external hardware. This paper proposes a novel approach to exploit a-priori knowledge of a cylindrical shaped inspection object, such as an aircraft fuselage, in order to support and supplement the position state estimation of the UAV. The relative localization approach comprises shape fitting of a cylinder model into LiDAR measurements of the aircraft using Random Sample Consensus (RANSAC), preceded by an intensity-based prefiltering, resulting in a significant increase of performance. This method can be applied to individual LiDAR sweeps, enabling onboard computation in soft real-time with a cycle time of 100 ms. Moreover, with the help of the onboard LiDAR-derived relative positioning to the aircraft, the problem of continuous line of sight to any transmitter of an indoor navigation system (iGPS) is alleviated by supplementing an existing sensor fusion for absolute localization, while maintaining an accuracy requirement in the submeter range. The feasibility of the proposed approach is verified experimentally by evaluating measurements obtained in a hangar with a Boeing 737–500.