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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