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

• URL: http://arxiv.org/abs/2101.09553v1
• Date: Sat, 23 Jan 2021 18:40:08 GMT
• Title: Real-Time, Flight-Ready, Non-Cooperative Spacecraft Pose Estimation Using Monocular Imagery
• Authors: Kevin Black, Shrivu Shankar, Daniel Fonseka, Jacob Deutsch, Abhimanyu Dhir, and Maruthi R. Akella
• Abstract summary: This work presents a novel convolutional neural network (CNN)-based monocular pose estimation system. It achieves state-of-the-art accuracy with low computational demand. The system achieves real-time performance on low-power flight-like hardware.
• Score: 1.1083289076967897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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