  A key requirement for autonomous on-orbit proximity operations is the
estimation of a target spacecraft's relative pose (position and orientation).
It is desirable to employ monocular cameras for this problem due to their low
cost, weight, and power requirements. This work presents a novel convolutional
neural network (CNN)-based monocular pose estimation system that achieves
state-of-the-art accuracy with low computational demand. In combination with a
Blender-based synthetic data generation scheme, the system demonstrates the
ability to generalize from purely synthetic training data to real in-space
imagery of the Northrop Grumman Enhanced Cygnus spacecraft. Additionally, the
system achieves real-time performance on low-power flight-like hardware.
