Odometry Aided by a Star Tracker
Bibliographic record
Abstract
Abstract—This paper develops a practical framework for es-timating rover position in full-dark conditions by correcting relative odometric estimates with periodic, absolute-attitude measurements from a star tracker. The framework is validated using just under 2.5 kilometres of field data gathered at the University of Toronto’s Koffler Scientific Reserve at Jokers Hill (KSR) comprised of both wheel odometry and lidar-based Visual Odometry (VO). It is shown that for the wheel odom-etry solution, the final estimate of rover position was within 21 metres of the groundtruth as calculated by a differential GPS receiver, or 0.85 % of the total traverse distance. When the star tracker measurements are artificially limited to occurring ap-proximately every 250 metres, the algorithm still performs well, giving a final position error of 75.8 metres or 3.0%. Preliminary results to replace wheel odometry with lidar-based VO for the
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".