  Spacecraft pose estimation is an essential computer vision application that
can improve the autonomy of in-orbit operations. An ESA/Stanford competition
brought out solutions that seem hardly compatible with the constraints imposed
on spacecraft onboard computers. URSONet is among the best in the competition
for its generalization capabilities but at the cost of a tremendous number of
parameters and high computational complexity. In this paper, we propose
Mobile-URSONet: a spacecraft pose estimation convolutional neural network with
178 times fewer parameters while degrading accuracy by no more than four times
compared to URSONet.
