Two-axis scanning lidar geometric calibration using intensity imagery and distortion mapping
Bibliographic record
Abstract
Accurate pose estimation relies on high-quality sensor measurements. Due to manufacturing tolerance, every sensor (camera or lidar) needs to be individually calibrated. Feature-based techniques using simple calibration targets (e.g., a checkerboard pattern) have become the dominant approach to camera sensor calibration. Existing lidar calibration methods require a controlled environment (e.g., a space of known dimension) or specific configurations of supporting hardware (e.g., coupled with GPS/IMU). Leveraging recent state estimation developments based on lidar intensity imagery, this paper presents a calibration procedure for a two-axis scanning lidar using only an inexpensive checkerboard calibration target. In addition, the proposed method generalizes a two-axis scanning lidar as an idealized spherical camera with additive measurement distortions. Conceptually, this is not unlike normal camera calibration in which an arbitrary camera is modelled as an idealized projective (pinhole) camera with tangential and radial distortions. The resulting calibration method, we believe, can be readily applied to a variety of two-axis scanning lidars. We present the measurement improvement quantitatively, as well as the impact of calibration on a 1.1-km visual odometry estimate.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".