Extending high-angular accuracy to a near omni-directional 3D range sensor
Bibliographic record
Abstract
The emergence of tripod-mounted lidar sensors as a viable method of 3D data collection has provided users with the ability to interrogate structures using high-resolution, metrically accurate 3D measurements. As with any measurement device, the accuracy of the collected data is of paramount importance. Angular accuracy is a crucial parameter in the overall performance of a 3D range sensor. This is particularly true in long-range applications where angular errors would be amplified proportionately to the target range. Consequently, angular accuracy is the determining factor in the accuracy of a long-range tripod mounted laser scanner. Recent advances in laser scanning technologies enabled significant increases in the addressable field-of-view (FOV) of 3D scanners. The most common embodiment of such systems incorporates two axes-rotation mechanisms. Typically, a rapidly oscillating mirror directs a laser beam into a “sheet” of light covering a vertical plane. This plane, in turn, is rotated around a vertical axis to provide nearly omni-directional coverage of the scene. A 3D measurement system's angular accuracy depends on two angular characteristics: angular resolution and angular repeatability. The systems described above could suffer on both dimensions. In this case the angular resolution is determined by the available angular position sensors yielding angular increments that are too crude for long-range 3D measurements. Similarly, angular repeatability of the available actuators suffers from non-linearities and other mechanical instabilities. The combined results are a data set that is less accurate than what is achievable in small FOV systems of similar configuration.
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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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".