An Overlap-Free Calibration Method for LiDAR-Camera Platforms Based on Environmental Perception
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".