Comparative Analysis of Different Approaches for Multi-camera System Calibration
Bibliographic record
Abstract
Mobile mapping systems integrate a set of imaging sensors and a position and orientation system. To fully achieve the accuracy of the utilized sensors, a careful system calibration should be carried out. The system calibration involves individual sensor calibration and the mounting parameters calibration relating the system components. For multi-camera systems, the mounting parameters involve two sets of relative orientation parameters (ROP): the ROP among the cameras and the ROP between the cameras and the navigation sensors. This paper proposes a mathematical model for a single-step photogrammetric system calibration suitable for both single and multi-camera systems. As a special case of this model, indirect geo-referencing can be performed with relative orientation constraints (ROC). A general model which allows the estimation of ROP as wells as the incorporation of prior information on the ROP among the cameras during the integrated sensor orientation (ISO) is also proposed. This general model can be used for the indirect georeferencing with ROC as well as ISO without ROP among the cameras. To evaluate the performance of the single-step system calibration using the different models, real dataset captured by a hand-held multi-camera system is used. The system calibration is performed by using five different models and the performance is compared among these models.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".