Bibliographic record
Abstract
The goal of this work is to investigate the benefits of using a well-known nonlinear motion estimator, an Extended Kalman Filter (EKF), in conjunction with the Iterative Closest Point algorithm (ICP}, in particular, for the purpose of tracking the pose of a target satellite using a chaser satellite equipped with a LIDAR sensor. To accomplish this goal, two different architectures for tracking the pose of a target satellite were first implemented in MATLAB Simulink, and then implemented and tested on the Canadian Space Agency Automated Robotics Test Bed (CART} at the Canadian Space Agency (CSA} using a Neptec Laser Camera System as a sensor. The two architectures are: a} a pose tracking architecture that accepts the estimated pose supplied by the EKF to provide an initial pose guess to the ICP algorithm; and b) a pose tracking architecture that uses the pose supplied by the pervious pose measurement from the ICP algorithm as the initial pose guess for the ICP algorithm. The pose estimator combine with the EKF was able to track an object with a higher rate of motion then the rate possible without a nonlinear estimator. When the EKF estimate of the target satellite's states converges, a decrease in the number of ICP iterations per sensor measurement was also observed. Furthermore, the EKF increased the robustness of the system allowing the system to continue tracking after blackout periods. The test results showed an increased level of robustness of the tracking architecture that utilizes a nonlinear estimator in conjunction with the ICP algorithm. The advantages of the use of EKF were observed both in a simulated environment and experimentation.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".