A Universal Framework for Extrinsic Calibration of Camera, Radar, and LiDAR
Bibliographic record
Abstract
Accurate extrinsic calibration of camera, radar, and LiDAR is critical for multi-modal sensor fusion in autonomous vehicles and mobile robots. Existing methods typically perform pair-wise calibration and rely on specialized targets, limiting scalability and flexibility. We introduce a universal calibration framework based on an Iterative Best Match (IBM) algorithm that refines alignment by optimizing correspondences between sensors, eliminating traditional point-to-point matching. IBM naturally extends to simultaneous camera-LiDAR-radar calibration and leverages tracked natural targets (e.g., pedestrians) to establish cross-modal correspondences without predefined calibration markers. Experiments on a realistic multi-sensor platform (fisheye-camera, LiDAR, and radar) and the KITTI dataset validate the accuracy, robustness, and efficiency of our method.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".