Abstract: This disclosure provides system and method for performing an autonomous aircraft visual inspection task using an unmanned aerial vehicle (UAV). The UAV is equipped with a front-facing RGB-D camera, one Velodyne three dimensional Light Detection and Ranging with 64 channels, and one Inertial Measurement Unit. In the method of the present disclosure, the UAV takeoff from any nearby location of the aircraft and face the RGB-D camera towards the aircraft. The UAV find the nearest landmark using a template matching approach and register with the aircraft coordinate system. The UAV navigate using LiDAR and IMU measurements, whereas the inspection process uses measurements from the RGB-D camera. The UAV navigate using a proposed safe navigation around the aircraft by avoiding obstacles. The system identifies the objects of interest using a deep-learning based object detection tool and then performs the inspection. A simple measuring algorithm for simulated objects of interest is implemented.

Abstract: This disclosure provides system and method for performing an autonomous aircraft visual inspection task using an unmanned aerial vehicle (UAV). The UAV is equipped with a front-facing RGB-D camera, one Velodyne three dimensional Light Detection and Ranging with 64 channels, and one Inertial Measurement Unit. In the method of the present disclosure, the UAV takeoff from any nearby location of the aircraft and face the RGB-D camera towards the aircraft. The UAV find the nearest landmark using a template matching approach and register with the aircraft coordinate system. The UAV navigate using LiDAR and IMU measurements, whereas the inspection process uses measurements from the RGB-D camera. The UAV navigate using a proposed safe navigation around the aircraft by avoiding obstacles. The system identifies the objects of interest using a deep-learning based object detection tool and then performs the inspection. A simple measuring algorithm for simulated objects of interest is implemented.