Visual Navigation for Rendezvous and Docking using PMD Camera
Bibliographic record
Abstract
There is one common problem of satellites which quite widely discussed: the lifespan. An idea to extend operational lifespan of a satellite in orbit, instead of replacing it by a new one, is concluded in On-Orbit Servicing (OOS) projects. The typical scenario of OOS is safe and reliable Rendezvous and Docking (RvD) of the approaching chaser to the target and taking over attitude control. In this paper relative navigation using new product Photonic Mixer Device (PMD), as new sort of the 3D Time-of-Flight (ToF) sensor, is suggested and of great interest, because the PMD sensor has never been used in space environment before. For optical navigation based on using a PMD camera, the following tasks and challenges have to be considered: initial pose estimation with a subsequent real-time object tracking, extension of the measurement range of the PMD sensor, camera calibration, performance measurements with a RvD simulation facility like EPOS 2.0 (European Proximity Operations Simulator). This paper mainly focuses on the first objective, namely pose estimation with a PMD sensor. Before calculating the pose estimate, the problem of actually finding target must be solved. The model based matching algorithm is proposed in this paper for 3D target acquisition and estimation of 6 degrees of freedom (3D position and 3D orientation) using data measurements from PMD camera.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".