A Study on In Situ Calibration of an Off-The-Shelf Digital Camera Integrated to a Lidar System
Bibliographic record
Abstract
Photogrammetric procedures using the integration of imagery and light detection and ranging (Lidar) datasets are becoming common in mapping companies nowadays. When photogrammetric and Lidar surveys are performed simultaneously while having the camera connected to the Lidar system, the image's exterior orientation parameters computed from the Lidar system can be used to perform photogrammetric applications. To implement such approach, camera calibration procedures should be applied to obtain accurate interior orientation parameters. This paper shows the performed study and experimental results from an in situ self-calibration using simultaneously collected imagery and Lidar datasets. The experimental study is conducted to devise a methodology for the in situ self-calibration that uses a set of vertical control points, extracted from Lidar point cloud, as well as the three-dimensional coordinates of the camera exposure stations, computed from the Lidar trajectory. For the small photogrammetric block used in this study, the results from the performed experiments demonstrated that the in situ self-calibration is required to obtain horizontal accuracies of approximately one image pixel on the ground in bundle adjustment assisted by direct measured of the coordinates of the camera exposure stations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".