  On-board estimation of the pose of an uncooperative target spacecraft is an
essential task for future on-orbit servicing and close-proximity formation
flying missions. However, two issues hinder reliable on-board monocular vision
based pose estimation: robustness to illumination conditions due to a lack of
reliable visual features and scarcity of image datasets required for training
and benchmarking. To address these two issues, this work details the design and
validation of a monocular vision based pose determination architecture for
spaceborne applications. The primary contribution to the state-of-the-art of
this work is the introduction of a novel pose determination method based on
Convolutional Neural Networks (CNN) to provide an initial guess of the pose in
real-time on-board. The method involves discretizing the pose space and
training the CNN with images corresponding to the resulting pose labels. Since
reliable training of the CNN requires massive image datasets and computational
resources, the parameters of the CNN must be determined prior to the mission
with synthetic imagery. Moreover, reliable training of the CNN requires
datasets that appropriately account for noise, color, and illumination
characteristics expected in orbit. Therefore, the secondary contribution of
this work is the introduction of an image synthesis pipeline, which is tailored
to generate high fidelity images of any spacecraft 3D model. The proposed
technique is scalable to spacecraft of different structural and physical
properties as well as robust to the dynamic illumination conditions of space.
Through metrics measuring classification and pose accuracy, it is shown that
the presented architecture has desirable robustness and scalable properties.
