A strip adjustment procedure to mitigate the impact of inaccurate mounting parameters in parallel lidar strips
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Lidar (laser scanning) technology has been proven as a prominent technique for the acquisition of high‐density and accurate topographic information. Because of systematic errors in the lidar measurements (drifts in the position and orientation information and biases in the mirror angles and ranges) and/or in the parameters relating the system components (mounting parameters), adjacent lidar strips may exhibit discrepancies. Although position and orientation drifts can have a more significant impact, these errors and their impact do not come as a surprise if the quality of the GPS/INS integration process is carefully examined. Therefore, the mounting errors are singled out in this work. The ideal solution for improving the compatibility of neighbouring strips in the presence of errors in the mounting parameters is the implementation of a rigorous calibration procedure. However, such a calibration requires the original observations, which may not be usually available. In this paper, a strip adjustment procedure to improve the compatibility between parallel lidar strips with moderate flight dynamics (for example, acquired by a fixed‐wing aircraft) over an area with moderately varying elevation is proposed. The proposed method is similar to the photogrammetric block adjustment of independent models. Instead of point features, planar patches and linear features, which are represented by sets of non‐conjugate points, are used for the strip adjustment. The feasibility and the performance of the proposed procedure together with its impact on subsequent activities are illustrated using experimental results from real data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it