Autonomous satellite rendezvous and docking using lidar and model based vision
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
Servicing satellites on-orbit requires ability to rendezvous and dock by an unmanned spacecraft with no or minimum human input. Novel imaging sensors and computer vision technologies are required to detect a target spacecraft at a distance of several kilometers and to guide the approaching spacecraft to contact. Current optical systems operate at much shorter distances, provide only bearing and range towards the target, or rely on visual targets. Emergence of novel LIDAR technologies and computer vision algorithms will lead to a new generation of rendezvous and docking systems in the near future. Such systems will be capable of autonomously detecting a target satellite at a distance of a few kilometers, estimating its bearing, range and relative orientation under virtually any illumination, and in any satellite pose. At MDA Space Missions we have developed a proof-of-concept vision system that uses a scanning LIDAR to estimate pose of a known satellite. First, the vision system detects a target satellite, and estimates its bearing and range. Next, the system estimates the full pose of the satellite using a 3D model. Finally, the system tracks satellite pose with high accuracy and update rate. Estimated pose provides information where the docking port is located even if the port is not visible and enables selecting more efficient flight trajectory. The proof-of-concept vision system has been integrated with a commercial time-of-flight LIDAR and tested using a moving scaled satellite replica in the MDA Vision Testbed.
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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 | 0.001 |
| 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