AIRBORNE LIDAR: A FULLY-AUTOMATED SELF-CALIBRATION PROCEDURE
Bibliographic record
Abstract
Abstract. Automated calibration of LIDAR systems has been an active field of research and development over the last years. Traditional calibration approaches rely on manual extraction of geometric features in the laser data and require time-intensive input of a trained operator. Recently, new methodologies evolved using automatic extraction of linear features and planar information to minimize systematic errors in LIDAR strips. This paper presents a new methodology of LIDAR calibration using automatically reconstructed planar features. The calibration approach presented herein integrates the physical sensor model and raw laser measurements and allows for refined calibration of internal system parameters. The new methodology is tested and compared with a traditional approach based on manual boresighting using a typical survey mission. Optech's software suite LMS, which is the first commercial implementation of this functionality, was used to process the data and to derive means of quality assessment. Different methods of reconstructing automatically extracted geometric features are presented and discussed in the context of their contribution to the calibration process. The final results are compared numerically and through graphic quality check.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".