System Self-Calibration Model for Non-Vertical Four-Prism Airborne LiDAR
Bibliographic record
Abstract
System errors in light detection and ranging (LiDAR) systems cannot be ignored due to their influence on geo-referencing accuracy. Previous calibration methods are based on the vertical LiDAR system and do not simultaneously consider the bore-sight angles, lever-arm errors, angles, range, trajectory position, and angle correction parameters for each strip. This study proposes a system self-calibration model based on a high-precision positioning model for the non-vertical four-prism airborne LiDAR system. Furthermore, an automatic system calibration process is presented using this model that identifies 12 parameters for overlapping LiDAR data. Also, each strip of trajectory data has three position corrections and three angle corrections. The feasibility and applicability of the method is demonstrated via qualitative and quantitative analyses using real data from plain and hilly areas. The experimental results prove that the proposed method is stable and reliable. Specifically, the proposed method can compensate for system bias, correct original flight data, and provide seamless data. This model can be applied to actual flight engineering datasets, has no terrain limitations, no specific requirements for the trajectory configuration, no specific hypotheses, and no need for control information.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".