Relative Pose Determination of a Non-Cooperative Spacecraft using Thermal Imagery
Bibliographic record
Abstract
Accurate and reliable pose determination of a non-cooperative spacecraft is a critical requirement for applications of on-orbit servicing, orbital debris removal, and in-space assembly missions.Recent advances in deep learning and the increasing availability of open-source datasets have enabled researchers to advance the work in this field by experimenting with deep learning algorithms.To date, all open-source datasets use imagery in the visible spectrum; however, visible imagery has a known limitation where the models perform poorly in harsh lighting conditions.This research introduces a novel lab-generated ML dataset for spacecraft pose estimation using thermal imagery rather than visible as a potential solution to the problem.Additionally, a design for a "light-weight" Convolutional Neural Network architecture is introduced, which uses 50% fewer parameters than state-of-the-art architectures in the field and demonstrates performance comparable to a typical architecture on the novel dataset.I would like to express my deepest gratitude to my supervisor, Dr. Steve Ulrich, for his guidance, mentorship, and patience throughout this journey.His feedback and constant encouragement challenged me to push the boundaries of my research, for which I am extremely grateful.I would also like to thank my peers at the Spacecraft Robotics and Control lab who taught me about the lab, helped me with dataset collection, and for offering many insightful brainstorming sessions for my experiments.Finally, I want to
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".