Stereo vision based swing angle sensor for mining rope shovel
Bibliographic record
Abstract
An easily retrofittable stereo vision based system for quick and temporary measurement of a mining shovel's swing angle is presented. The stereo camera is mounted externally to the upper swingable shovel house, with a clear view of the shovel's lower carbody. As the shovel swings from its 0° swing angle position, the camera revolves with the shovel house, seeing differing views of the carbody. In real-time, the camera position is tracked, which in turn is used to calculate the swing angle. The problem was solved using the Simultaneous Localization and Mapping (SLAM) approach in which the system learns a map of 3D features on the carbody while using the map to determine the camera pose. The contribution includes a locally maximal Harris corner selection technique and a novel use of 3D feature clusters as landmarks, for improving the robustness of visual landmark matching in an outdoor environment. Results show that the vision-based sensor has a maximum error of +/- 1° upon map convergence.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".