Visual teach and repeat for long‐range rover autonomy
Bibliographic record
Abstract
Abstract This paper describes a system built to enable long‐range rover autonomy using a stereo camera as the only sensor. During a learning phase, the system builds a manifold map of overlapping submaps as it is piloted along a route. The map is then used for localization as the rover repeats the route autonomously. The use of local submaps allows the rover to faithfully repeat long routes without the need for an accurate global reconstruction. Path following over nonplanar terrain is handled by performing localization in three dimensions and then projecting this down to a local ground plane associated with the current submap to perform path tracking. We have tested this system in an urban area and in a planetary analog setting in the Canadian High Arctic. More than 32 km was covered—99.6% autonomously—with autonomous runs ranging from 45 m to 3.2 km, all without the use of the global positioning system (GPS). Because it enables long‐range autonomous behavior in a single command cycle, visual teach and repeat is well suited to planetary applications, such as Mars sample return, in which no GPS is available. © 2010 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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".