Suppressing Laser Triangulation Sensors Optical Aberrations by Replacing the Lens with a Slit
Bibliographic record
Abstract
This paper presents a new capture method for laser triangulation sensors consisting in replacing the lens with a slit—effectively making it a pinhole camera—and exploiting diffraction effects. This new method circumvents optical aberrations such as spherical, defocusing, coma, field curvature, and lens distortion, as well as lens flare to a point where it can be virtually ignored. Moreover, using a slit reduces the number of optical parts and simplifies the modeling of the laser spot to find its center. To test our proposed method, we generated data sets taking pictures with both a lens and a slit on different materials placed on a worm gear with its position controlled by a high-precision step motor. With this data, we extracted the center of the laser spot with different image processing algorithms. It was then possible to assess the goodness of fit of the triangulation curve using the sets of captured points by both methods. We show that not only we circumvent the adverse effects of optical aberrations and lens flares, we obtain a more accurate estimation of the actual known distance of the target.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".