Planetary Rover Visual Motion Estimation improvement for Autonomous, Intelligent, and Robust Guidance, Navigation and Control
Bibliographic record
Abstract
This paper presents the Mojave Desert field test results of an improved planetary rover visual motion estimation technique for the Autonomous, Intelligent, and Robust Guidance, Navigation, and Control for Planetary Rovers (AIR-GNC). The main improvements include: optimal use of different features from stereo-pair images as visual landmarks, and the use of VME-based feedback to close the path tracking loop. As well, a long-range and wide FOV active 3D sensor was used to extract long-range fixed landmarks for enabling visual motion estimation observability, and thus improving the accuracy of the VME. The field test, conducted in relevant Mars-like terrains, under dramatically changing weather and lighting conditions, shows good localization accuracy on average. Moreover, the MDA developed Enhanced IMU-corrected odometry was reliable and had good accuracy in all test locations including in loose sand dunes. These results are based on data collected during 7.3 km of traverses, under both fully autonomous and tele-operated control. 1
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".