3D LASSO: REAL-TIME POSE ESTIMATION FROM 3D DATA FOR AUTONOMOUS SATELLITE SERVICING
Bibliographic record
Abstract
The recent development of space flight ready 3D sensors, such as the Neptec Laser Camera System (LCS), allows 3D vision technology to be considered for autonomous missions. These active sensors provide their own illumination and have a small instantaneous field of view, making them immune to dynamic lighting. Harsh and dynamic lighting conditions have severely limited the use of 2D passive camera based space vision systems for mission critical applications. Autonomous robotic servicing missions, such as the Hubble Rescue Vehicle (HRV), will require vision systems that are capable of providing high accuracy pose estimates in real-time while being robust to changes in lighting conditions. This paper describes the 3-Dimensional LCS Algorithms for Spacecraft Servicing On-orbit ( 3D LASSO) system currently under development at Neptec. The project is funded by the Canadian Space Agency (CSA) under the Space Technologies Development Program (STDP). The 3D LASSO system is designed to perform real-time tracking and 6 degree of freedom pose estimation of target spacecraft(s) from sparse and noisy 3D data. The approach is compatible with any sensor capable of providing 3D data. The algorithms have been successfully tested with Neptec’s LCS in a variety of test scenarios. Tracking was performed using the random access capability of the sensor which is used to perform rapid, sparse sampling of the target object(s). The data obtained is aligned to a reference model of the target(s) using a newly developed faster version of the Iterative Closest Point (ICP) algorithm developed at Neptec. The pose estimate obtained is then used to compute the trajectory of the object(s).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.002 |
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".