An Overlap-Free Calibration Method for LiDAR-Camera Platforms Based on Environmental Perception
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Bibliographic record
Abstract
Indoor environments are challenging for multisensor calibrations. Traditional calibration methods use the target structure for camera and LiDAR calibration. Those approaches not only require pre-processed data and offline calculations, but also face challenges in low-light and object-occluded indoor environments. We proposed an automatic calibration method using trajectory constraints on the LiDAR-Camera. The proposed method first obtains the accurate LiDAR trajectory by the LiDAR-SLAM (LIO-SAM) algorithm. At the same time, the problem of visual SLAM trajectory drift in the indoor environment is improved by graphical optimization using the rigid relative position invariance between sensors during displacement. Thus, extrinsic calibration is achieved by using the relative relationship between sensor trajectories. This method has higher robustness than the target-based calibration methods. The experimental results show that our algorithm has higher accuracy than the target-based calibration in the underground environment. The rotation root-mean-square error (RMSE) improves from 6.637° to 0.564°, and the translation RMSE improves from 0.197 to 0.082 m.
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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