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.

翻译：通用视觉检查是一种定期使用的人工检查流程，用于检测和定位商用飞机外部明显的损伤。在登机口执行该流程的需求日益增加，以最大限度减少飞机停场时间，且自动化此过程可降低对人力的依赖。该自动化通常需要第一步估计相机相对于飞机的位姿以完成初始化。然而，定位方法往往需要基础设施支持，在不受控的户外环境和机场停机坪有限的周转时间（约2小时）内实施极具挑战性。此外，商用飞机的访问权限极为有限，导致解决方案的开发和测试面临困难。为此，本文提出了一种无需现场基础设施的初始化方法，通过使用执行检查任务时使用的同一台云台变焦相机来估计自身位姿。该方法采用仅通过合成图像训练的深度卷积神经网络回归相机位姿。在生成数据集训练网络时应用域随机化技术，并通过在现有损失函数中引入利用已知飞机几何结构关联位置和方向的新组件来提高预测精度。实验结果表明，我们成功实现了相机位姿回归，中位误差为0.22米和0.73度。