  This paper introduces a deep transformer network for estimating the relative
6D pose of a Unmanned Aerial Vehicle (UAV) with respect to a ship using
monocular images. A synthetic dataset of ship images is created and annotated
with 2D keypoints of multiple ship parts. A Transformer Neural Network model is
trained to detect these keypoints and estimate the 6D pose of each part. The
estimates are integrated using Bayesian fusion. The model is tested on
synthetic data and in-situ flight experiments, demonstrating robustness and
accuracy in various lighting conditions. The position estimation error is
approximately 0.8\% and 1.0\% of the distance to the ship for the synthetic
data and the flight experiments, respectively. The method has potential
applications for ship-based autonomous UAV landing and navigation.
