LIDAR SYSTEM CALIBRATION: IMPACT ON PLANE SEGMENTATION AND PHOTOGRAMMETRIC DATA REGISTRATION
Bibliographic record
Abstract
The availability of 3D surface data is crucial for several industrial, public, and military applications. Light Detection And Ranging (LiDAR) is an active sensor system capable of collecting 3D information from an object surface using laser pulses. Accurate and dense LiDAR data can be utilized for georeferencing of photogrammetric data and segmentation of 3D buildings. LiDAR data contaminated by systematic errors cannot guarantee the achievement of the expected accuracy and discrepancies might occur between overlapping strips. This paper presents an alternative method for LiDAR system calibration. In the proposed method, biases in LiDAR system parameters are estimated using time-tagged point cloud and trajectory data (position only). Unlike conventional calibration methods, the proposed method does not require raw measurements like GPS/INS observations, scan-mirror angles, and laser ranges for the laser footprints. The influence of LiDAR system calibration is analyzed through the evaluation of the relative and absolute accuracy before/after the calibration. Before calibration, segmentation procedure may produce unexpected planes because of positional errors of laser footprints and discrepancies between overlapping strips. For relative accuracy analysis, the plane segmentation result before the calibration will be compared to the planes segmented from re-constructed point cloud using the estimated biases in the system parameters. In addition, the impact of the LiDAR system calibration on the absolute accuracy of the point cloud is evaluated by using the LiDAR data for photogrammetric georeferencing before and after performing the proposed calibration procedure. Alternative primitives, such as straight lines extracted from the LiDAR point cloud are used as the control information. The absolute accuracy is evaluated through check point analysis when the photogrammetric reconstruction is done using the original/calibrated LiDAR features.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".