Automatic Tracking Initialization from TriDAR data for Autonomous Rendezvous & Docking
Bibliographic record
Abstract
Neptec has developed a vision system for autonomous on-orbit rendezvous and docking that operates from 3D data and does not require cooperative targets. The system combines a TriDAR active 3D sensor and a model based tracking algorithm to calculate relative pose information (6 DOF) in real-time. In collaboration with Laval University and the Canadian Space Agency (CSA), techniques to localize an object in space from 3D data have been developed. These algorithms are necessary to automatically initiate the tracking system and recover if tracking lock is lost. The first approach developed, called Polygonal Aspect Hashing, was designed to operate directly from a sparse disorganized 3D point cloud. This takes advantage of the random access nature of the TriDAR sensor that can acquire data using localized fast scanning waveforms. The second technique developed, uses a geometric hashing feature matching approach.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".