Global rover localization by matching lidar and orbital 3D maps
Bibliographic record
Abstract
Current rover localization techniques such as visual odometry have proven to be very effective on short to medium-length traverses (e.g., up to a few kilometres). This paper deals with the problem of long-range rover localization (e.g., 10km and up). An autonomous method to globally localize a rover is proposed by matching features detected from a 3D orbital elevation map and rover-based 3D lidar scans. The accuracy and efficiency of the algorithm is enhanced with visual odometry, and inclinometer/sun-sensor orientation measurements. The methodology was tested with real data, including 37 lidar scans of terrain from a Mars-Moon analogue site on Devon Island, Nunavut. When a scan contained a sufficient number of good topographic features, localization produced position errors of no more than 100m, and as low as a few metres in many cases. On a 10km traverse, the developed algorithm's localization estimates were shown to significantly outperform visual odometry estimates. It is believed that this architecture could be used to accurately and autonomously localize a rover on long-range traverses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".