Visual path following on a manifold in unstructured three-dimensional terrain
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
This paper describes the design and testing of a technique to enable long-range autonomous navigation using a stereo camera as the only sensor. During a learning phase, the rover is piloted along a route capturing stereo images. The images are processed into a manifold map of topologically-connected submaps that may be used for localization during an autonomous repeat traverse. Path following in non-planar terrain is handled by moving from localization in three dimensions, to path following in two dimensions using a local ground plane associated with each submap. The use of small submaps decouples the computational complexity of route repeating from the length of the path. We validate the algorithm by demonstrating its performance on a difficult three-dimensional route. Using this technique, a rover may autonomously traverse a multi-kilometer route in unstructured, three-dimensional terrain, without an accurate global reconstruction.
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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