Real-time 3D vision solution for on-orbit autonomous rendezvous and docking
Bibliographic record
Abstract
Neptec has developed a vision system for the capture of non-cooperative objects on orbit. This system uses an active TriDAR sensor and a model based tracking algorithm to provide 6 degree of freedom pose information in real-time from mid range to docking. This system was selected for the Hubble Robotic Vehicle De-orbit Module (HRVDM) mission and for a Detailed Test Objective (DTO) mission to fly on the Space Shuttle. TriDAR (triangulation + LIDAR) technology makes use of a novel approach to 3D sensing by combining triangulation and Time-of-Flight (ToF) active ranging techniques in the same optical path. This approach exploits the complementary nature of these sensing technologies. Real-time tracking of target objects is accomplished using 3D model based tracking algorithms developed at Neptec in partnership with the Canadian Space Agency (CSA). The system provides 6 degrees of freedom pose estimation and incorporates search capabilities to initiate and recover tracking. Pose estimation is performed using an innovative approach that is faster than traditional techniques. This performance allows the algorithms to operate in real-time on the TriDAR's flight certified embedded processor. This paper presents results from simulation and lab testing demonstrating that the system's performance meets the requirements of a complete tracking system for on-orbit autonomous rendezvous and docking.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".