Evaluating Smartphone Camera Calibration Configurations and their Effects on SLAM
Bibliographic record
Abstract
Abstract. Smartphones have become increasingly viable for photogrammetric and simultaneous localization and mapping (SLAM) applications due to their portability and widespread availability. However, the repeatability of smartphone camera calibration remains a concern, as intrinsic orientation parameters (IOPs) can vary significantly between calibration attempts due to innate software and hardware corrective mechanisms. This study investigates the impact of calibration grid type, grid size, and distortion modelling on smartphone camera calibration uncertainty and its downstream effects on positioning accuracy in monocular SLAM. Using three smartphone models (iPhone 14 and two Google Pixel 7 devices), we conducted a comprehensive analysis of 24 calibration configurations processed through Kalibr. The estimated IOPs were then applied in a monocular ORB-SLAM3 pipeline, and the resulting trajectories were compared against a high-precision integrated LiDAR inertial ground truth. These findings provide insights into optimizing smartphone calibration setups, which has effects on SLAM-based applications in mobile mapping, robotics, and augmented reality.
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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 | 0.008 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".