Uncooperative Spacecraft Pose Estimation Using Monocular Monochromatic Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Imaging cameras are cost-effective sensors for spacecraft navigation. Image-driven techniques to extract the target spacecraft from its background are efficient and do not require pretraining. In this paper, we introduce several image-driven foreground extraction methods, including combining the difference of Gaussian-based scene detection and graph manifold ranking-based foreground saliency generation. We successfully apply our foreground extraction method on infrared images from the STS-135 flight mission captured by the space shuttle’s Triangulation and LIDAR Automated Rendezvous and Docking System (TriDAR) thermal camera. Our saliency approach demonstrates state-of-the-art performance and provides an order of magnitude reduction in processing speed from the traditional methods. Furthermore, we develop a new uncooperative spacecraft pose estimation method by combining our foreground extraction technique with the level-set region-based pose estimation with novel initialization and gradient descent enhancements. Our method is validated using synthetically generated Envisat, Radarsat model, and International Space Station motion sequences. The proposed process is also validated with real rendezvous flight images of the International Space Station.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it