Field trial results of planetary rover visual motion estimation in Mars analogue terrain
Bibliographic record
Abstract
Abstract This paper presents the Mojave Desert field test results of planetary rover visual motion estimation (VME) developed under the “Autonomous, Intelligent, and Robust Guidance, Navigation, and Control for Planetary Rovers (AIR‐GNC)” project. Three VME schemes are compared in realistic conditions. The main innovations of this project include the use of different features from stereo‐pair images as visual landmarks and the use of vision‐based feedback to close the path‐tracking loop. The multiweek field campaign, conducted on relevant Mars analogue terrains, under dramatically changing lighting and weather conditions, shows good localization accuracy on the average. Moreover, the MDA‐developed inertial measurement unit (IMU)‐corrected odometry was reliable and had good accuracy at all test locations, including loose sand dunes. These results are based on data collected during 7.3 km of traverse, including both fully autonomous and joystick‐driven runs. © 2012 Wiley Periodicals, Inc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".