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

Real-Time, Flight-Ready, Non-Cooperative Spacecraft Pose Estimation Using Monocular Imagery