Initialisation of Autonomous Aircraft Visual Inspection Systems via CNN-Based Camera Pose Estimation

Abstract

General Visual Inspection is a manual inspection process regularly used to detect and localise obvious damage on the exterior of commercial aircraft. There has been increasing demand to perform this process at the boarding gate to minimize the downtime of the aircraft and automating this process is desired to reduce the reliance on human labour. This automation typically requires the first step of estimating a camera's pose with respect to the aircraft for initialisation. However, localisation methods often require infrastructure, which can be very challenging when performed in uncontrolled outdoor environments and within the limited turnover time (approximately 2 hours) on an airport tarmac. In addition, access to commercial aircraft can be very restricted, causing development and testing of solutions to be a challenge. Hence, this paper proposes an on-site infrastructure-less initialisation method, by using the same pan-tilt-zoom camera used for the inspection task to estimate its own pose. This is achieved using a Deep Convolutional Neural Network trained with only synthetic images to regress the camera's pose. We apply domain randomisation when generating our dataset for training our network and improve prediction accuracy by introducing a new component to an existing loss function that leverages on known aircraft geometry to relate position and orientation. Experiments are conducted and we have successfully regressed camera poses with a median error of 0.22 m and 0.73 degrees.