Global Descriptors for Visual Pose Estimation of a Noncooperative Target in Space Rendezvous
Bibliographic record
Abstract
This article revisits methods based on global descriptors to estimate the pose of a known object using a monocular camera, in the context of space rendezvous between an autonomous spacecraft and a noncooperative target. These methods estimate the pose by detection, i.e., they do not require any prior information about the pose of the observed object, making them suitable for initial pose acquisition and the monitoring of faults in other on-board estimators. We consider here specifically methods that retrieve the pose of a known object using a precomputed set of invariants and geometric moments. Three classes of global invariant features are analyzed, based on complex moments, Zernike moments, and Fourier descriptors. The robustness, accuracy, and computational efficiency of the different invariants are tested and compared under various conditions. We also discuss certain implementation aspects of the method that lead to improved accuracy and efficiency over previously reported results. Overall, our results can be used to identify which variations of the method offer a sufficiently fast and robust solution for pose estimation by detection, with low computational requirements that are compatible with space-qualified processors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".