Vision Based Modeling and Localization for Planetary Exploration Rovers
Bibliographic record
Abstract
Exploration of large unknown planetary environments will rely on rovers that can autonomously cover distances of kilometres and maintain precise information about their location with respect to local features. During such traversals, the rovers will create photo-realistic three dimensional (3D) models of visited sites for autonomous operations on-site and mission planning on Earth. Currently rover position is estimated using wheel odometry, which is sufficient for short traversals but as error accumulates quickly, it is unsuitable for long distances. At MD Robotics, we are working on imaging technologies for future planetary rover missions. Two complementary technologies are currently investigated: a stereo based vision system and a scanning time-of-flight LIDAR system. Both imaging systems have been installed on board of two experimental rovers and tested in laboratory and outdoor environments. With stereo cameras, the rover can create photo-realistic 3D model as well as provide visual odometry that is more accurate than the rover dead reckoning. With the LIDAR, the rover can match 3D scans to estimate the relative location to improve the wheel and visual odometry. 1
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".