AC/DCC : Accurate Calibration of Dynamic Camera Clusters for Visual SLAM
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
In order to relate information across cameras in a Dynamic Camera Cluster (DCC), an accurate time-varying set of extrinsic calibration transformations need to be determined. Previous calibration approaches rely solely on collecting measurements from a known fiducial target which limits calibration accuracy as insufficient excitation of the gimbal is achieved. In this paper, we improve DCC calibration accuracy by collecting measurements over the entire configuration space of the gimbal and achieve a 10X improvement in pixel re-projection error. We perform a joint optimization over the calibration parameters between any number of cameras and unknown joint angles using a pose-loop error optimization approach, thereby avoiding the need for overlapping fields-of-view. We test our method in simulation and provide a calibration sensitivity analysis for different levels of camera intrinsic and joint angle noise. In addition, we provide a novel analysis of the degenerate parameters in the calibration when joint angle values are unknown, which avoids situations in which the calibration cannot be uniquely recovered. The calibration code will be made available at https://github.com/TRAILab/AC-DCC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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