Development of a low-cost ultra-tiny line laser range sensor
Bibliographic record
Abstract
To enable robotic sensing for tasks with requirements on weight, size, and cost, we develop an ultra-tiny line laser range sensor based on the Time-of-Flight (TOF) principle. With delicate circuit design and optical attachments, we create a sensor as small as 35[mm] × 27[mm] × 30[mm] and as light as 20[g]. The line sensor samples 272 pixels (256 effective pixels) uniformly distributed within the measurement field of view customizable using different laser lenses. The optimal measurement range of the sensor is 0.05[m] ~ 2[m]. Higher sampling rates can be achieved with a shorter range. The sensor can also extend its range to 3[m] with reduced accuracy. We model the overall errors of the sensor and formulate calibration methods, achieving repeatable accuracy and measurement bias both within 2[cm] with our tested ambient lighting conditions and measurement ranges. The sensor is applicable to range sensing tasks including humanoid hand-eye measurement, UAV safe landing, tiny robot range sensing, and object detection.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".