  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.
