3D surface mapping using a semi-autonomous rover: A planetary analog field experiment
Bibliographic record
Abstract
This paper describes a proposed operational architecture for a planetary worksite mapping mission concept. To map three-dimensional (3D) planetary terrain, we pro-pose to use a rover equipped with a laser rangefinder, and employ a stop-scan-go approach with a human-in-the-loop. In the operational cycle, the rover collects locally consis-tent 3D range data while stationary. The range data are coupled with visual odometry to estimate the rover pose at each scan and create a consistent 3D map. The 3D map is then used to evaluate candidate next-best views (NBV). The operator selects a NBV with the aid of three evalu-ation criteria and the rover autonomously travels to the NBV using a network of reusable paths (NRP). Finally, the rover collects another 3D scan and the cycle repeats. This mission concept was validated through hardware ex-periments on the CSA’s Mars Emulation Terrain (MET), which measures 60m × 120m and includes inclines, rocks, cliffs and a 5.5m-diameter crater.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".