Global rover localization by matching lidar and orbital 3D maps
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
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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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 | 0.000 |
| 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