Post-mission misalignment angle calibration for airborne laser scanners
Bibliographic record
Abstract
An Airborne Laser Scanning (ALS) system operates by locating returned laser pulses independently from all others. Locating the returned laser pulses requires knowing precisely for each laser pulse, the aircraft position (e.g. GPS), the attitude of the aircraft (e.g. IMU), the scanner angle when the laser pulse left the sensor, and the slant range to the terrain surface for that pulse. One of the most critical errors in ALS systems is the angular misalignment between the scanner and the IMU, which is called the misalignment or boresight error. This error must be addressed before an ALS system can accurately produce data. The purpose of this thesis was to develop and test a method of estimating the small misalignment angles between the laser scanner and the combined GPS/IMU solution for position and attitude. This method is semi-automated, requires no ground control and does not re-sample the ALS data in order to match the overlapping strips of data. A computer program called Misalignment Estimator was developed to estimate the misalignment angles using a least squares adjustment. The method was tested using a data set located at the Oshawa airport and provided by Optech. The misalignment angles were estimated to be -0.0178 degrees, -0.0829 degrees and 0.0320 degrees, for roll, pitch and heading respectively. The estimation of the misalignment angles was considered to be successful. Further research into automated point matching is recommended.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".