Estimation of biases in lidar system calibration parameters using overlapping strips
Bibliographic record
Abstract
Light detection and ranging (lidar) system calibration is essential to ensure the positional accuracy of the derived point cloud. Current lidar self-calibration techniques require full access to the system parameters and raw measurements (e.g., platform position and orientation, laser ranges, and scan mirror angles). Unfortunately, the raw measurements are not always available to the end-users. The absence of such information is limiting the widespread adoption of lidar calibration activities by the end-user. This paper proposes two alternative procedures for lidar system calibration, namely simplified and quasi-rigorous methods, which do not require the system raw measurements. The simplified method uses the lidar point cloud coordinates in overlapping parallel strips over terrain with moderate elevation variation to estimate biases in the system parameters. In this approach, the conventional lidar georeferencing equation is simplified based on a few reasonable assumptions. The quasi-rigorous method, on the other hand, is proposed to handle heading variations and varying terrain elevations using the time-tagged point coordinates and trajectory position data. Experimental results from simulated and real datasets showed that the proposed methods successfully estimated biases in system parameters, which produced more precise lidar points.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".