  Spacecraft pose estimation plays a vital role in many on-orbit space
missions, such as rendezvous and docking, debris removal, and on-orbit
maintenance. At present, space images contain widely varying lighting
conditions, high contrast and low resolution, pose estimation of space objects
is more challenging than that of objects on earth. In this paper, we analyzing
the radar image characteristics of spacecraft on-orbit, then propose a new deep
learning neural Network structure named Dense Residual U-shaped Network
(DR-U-Net) to extract image features. We further introduce a novel neural
network based on DR-U-Net, namely Spacecraft U-shaped Network (SU-Net) to
achieve end-to-end pose estimation for non-cooperative spacecraft.
Specifically, the SU-Net first preprocess the image of non-cooperative
spacecraft, then transfer learning was used for pre-training. Subsequently, in
order to solve the problem of radar image blur and low ability of spacecraft
contour recognition, we add residual connection and dense connection to the
backbone network U-Net, and we named it DR-U-Net. In this way, the feature loss
and the complexity of the model is reduced, and the degradation of deep neural
network during training is avoided. Finally, a layer of feedforward neural
network is used for pose estimation of non-cooperative spacecraft on-orbit.
Experiments prove that the proposed method does not rely on the hand-made
object specific features, and the model has robust robustness, and the
calculation accuracy outperforms the state-of-the-art pose estimation methods.
The absolute error is 0.1557 to 0.4491 , the mean error is about 0.302 , and
the standard deviation is about 0.065 .
