Comprehensive Analysis of System Calibration between Optical Camera and Range Finder
Bibliographic record
Abstract
This paper describes the comprehensive analysis of system calibration between an optical camera and a range finder. The results suggest guidelines for accurate and efficient system calibration enabling high-quality data fusion. First, self-calibration procedures were carried out using a testbed designed for both the optical camera and range finder. The interior orientation parameters of the utilized sensors were precisely computed. Afterwards, 92 system calibration experiments were carried out according to different approaches and data configurations. For comparison of the various experimental results, two measures, namely the matching rate of fusion data and the standard deviation of relative orientation parameters derived after system calibration procedures, were considered. Among the 92 experimental cases, the best result (the matching rate of 99.08%) was shown for the use of the one-step system calibration method and six datasets from multiple columns. Also, the root mean square values of the residuals after the self- and system calibrations were less than 0.8 and 0.6 pixels, respectively. In an overall evaluation, it was confirmed that the one-step system calibration method using four or more datasets provided more stable and accurate relative orientation parameters and data fusion results than the other cases.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".