POINT-BASED VERSUS PLANE-BASED SELF-CALIBRATION OF STATIC TERRESTRIAL LASER SCANNERS
Bibliographic record
Abstract
Abstract. Systematic trends are apparent when studying the self-calibration residuals of many modern static terrestrial laser scanners. Since the operation of terrestrial laser scanners is comparable to an efficient robotic total station, the sensor modelling parameters are developed in the spherical coordinate system where the raw observables of the scanner are range, horizontal angle, and vertical angle. Sensor calibration parameters are already well established for both hybrid and panoramic type laser scanners through the signalized target-based self-calibration method. In this paper, a less labour-intensive and more efficient planar-feature based terrestrial laser scanner self-calibration method, which is more suitable for in-situ self-calibration, is presented. Through simulation it will be demonstrated that the point-based self-calibration and plane-based self-calibration shares many common characteristics. Plane-based self-calibration results from real data captured with the panoramic type Leica HDS6100 and hybrid type Trimble GS200 scanner are also presented to show the practicality of the proposed method and as a comparison to the point-based self-calibration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".