Two-axis scanning lidar geometric calibration using intensity imagery and distortion mapping
Why this work is in the frame
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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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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