Design of a Spacecraft Pose Estimation System Using Convolutional Neural Networks

Design of a Spacecraft Pose Estimation System Using Convolutional Neural Networks

Fanizza, Vincenzo (TU Delft Aerospace Engineering)

Mooij, E. (mentor)
Dirkx, D. (graduation committee)
van Kampen, E. (graduation committee)

Delft University of Technology

Aerospace Engineering | Space Exploration

2023-11-28

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.

Space Debris
Space Exploration
Spacecraft Relative Navigation
Machine Learning
on-orbit servicing
Computer Vision
Pose estimation

http://resolver.tudelft.nl/uuid:f1b780ba-b7e9-4567-ba35-16ccd72b920c

2024-12-31

Student theses

master thesis

© 2023 Vincenzo Fanizza