Inferring geometry from imagery - Enabling high speed traversal
Bibliographic record
Abstract
Robotic vehicles operating in outdoor environments, commonly referred to as unmanned ground vehicles (UGV), are confronted with unstructured/semi-structured environments that are variable in nature. The geographical location significantly influences the environment's appearance, there are longer term seasonal cycles, as well as immediate affects such as the weather and lighting conditions. This environmental diversity has long caused researchers considerable grief, as developing a generalized terrain classification algorithm has proven to be very difficult. Researchers have skirted this problem by relying upon ranging sensors and constructing 2½D or, more recently, 3D world representations. Although geometric representations have been used extensively orientation errors limit the lookahead distance. An important UGV capability is high speed traversal, hence extending the lookahead distance that in turn increases the maximum attainable vehicle speed is an active area of research. This focus on high speed traversal in variable environments has pushed researchers to investigate techniques that allow learning from experience, in a more human like manner. This paper presents Defence R&D Canada - Suffield's progress in extending a 2½D world representation using vision and learning to infer geometry.
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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) | 0.001 | 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 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".