Automatic Extrinsic Calibration of Thermal Camera and LiDAR for Vehicle Sensor Setups
Bibliographic record
Abstract
LiDAR is one of the most used sensors in many areas like robotics, self-driving cars, and advanced driving assistance systems due to providing an accurate point cloud of the surroundings. However, to cope with challenges in perceiving the environment around a vehicle, LiDAR data is often combined with data from other sensors. Thermal cameras can provide complementary information that can be beneficial, especially for detecting pedestrians and seeing at nighttime and in fog, dust, etc. In this paper, we propose an algorithm for the extrinsic calibration of a thermal camera and a LiDAR sensor in a vehicle. First, one or more thermal image-point cloud pairs of our designed calibration target are collected. Then line and plane equations of the target’s edges and plane in both data modalities are found. Finally, the algorithm uses lines and plane correspondences to cross-calibrate the sensors. The proposed method obtains good results with one or more poses. We also show that it works well with sparse LiDAR data. Several experiments are presented to illustrate the effectiveness of the method.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".