Design of a Spacecraft Pose Estimation System Using Convolutional Neural Networks

Master thesis (2023)

Authors

V. Fanizza Aerospace Engineering

Contributors

E. Mooij Astrodynamics & Space Missions - Aerospace Engineering (mentor)
D. Dirkx Astrodynamics & Space Missions - Aerospace Engineering (graduation committee member)
E. van Kampen (graduation committee member)
Faculty
Aerospace Engineering, Aerospace Engineering
Copyright: © 2023 Vincenzo Fanizza
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Published Date

28-11-2023

Language

English

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Faculty

Aerospace Engineering

Abstract

Performing crucial activities for space exploration, e.g., debris removal and on-orbit servicing, systems for Rendezvous and Proximity Operations (RPO) are required to be autonomous and scalable. Within this context, learning-based relative navigation has gained significant traction due to the latest advancements in AI and the cost-effectiveness of monocular cameras.

This thesis introduces a pose estimation system composed of an Object Detection (OD) and a Keypoint Detection (KD) network for keypoint extraction, coupled with a pose solver, and trained purely on synthetic images. When applying realistic data augmentations, the system achieves a reduction in KD error by 80%, further improved by training on photorealistic images. After an architecture optimization step, the final system consistently meets the inference time requirements on the Myriad X edge processor. This proves the feasibility of developing RPO systems with artificial data, showcasing a scalable approach, while complying with the limitations of onboard hardware.

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VFanizza_FinalReport.pdf
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File under embargo until 31-12-2024