Integrated subsystem for Obstacle detection from a belt of micro-cameras
Bibliographic record
Abstract
This paper describes the on-going work on the design and the implementation of an integrated visual system dedicated to the classical function Obstacle detection. This system will be embedded on a mobile robot, which must navigate in a cluttered and dynamic environment: typically a public area like pedestrian streets, a transport station or a commercial center. The current robots are equipped typically by a belt of ultrasonic sensors or by Laser Range Finders: it is proposed here to evaluate how a set of micro-cameras mounted around a mobile robot could be used in order to detect free space and obstacles. During an off line learning step, the appearance-based characteristics of the ground are extracted and recorded; then on line, images are acquired synchronously by micro-cameras, every pixel on every image is classified as Ground or Obstacle from color and texture attributes, and geometrical constraints between successive images are applied in order to validate that detected obstacles are above the ground. Finally all information are fused in a single robot-centered occupancy grid. This paper presents algorithms proposed for ground classification and obstacle validation, and describes first results about the architecture proposed for the integrated system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".