Case Study: Rigorous Boresight Alignment of a Marine Mobile LiDAR System Addressing the Specific Demands of Port Infrastructure Monitoring
Bibliographic record
Abstract
Accurate infrastructure monitoring of ports and harbors is a vital operation conducted by the port’s authority. To operate regularly in this highly dynamic environment, we explore the potential of the cutting-edge mobile LiDAR systems (MLS) mounted on a vessel. To generate a high-quality 3 D point cloud that would satisfy the expected accuracy required in the monitoring task, the LiDAR scanner and the positioning and orientation system (POS) must be angularly aligned also known as boresight alignment. In this research, we introduce a boresight alignment methodology adapted to the port infrastructure surveillance based on prefabricated planar targets. After an analysis of planar target simulated data, we propose a boresight alignment site design. Then, we apply this boresight alignment site design in a real-world scenario. Obtained results estimate accurately roll and yaw angles errors with standard deviations of less than 0.002 degrees and pitch angle error with standard deviation less than 0.015 degrees. Finally, we defined a validation site and described the procedure that uses these features to validate the quality of the estimated parameters. The relative comparison of the georeferenced point clouds, before and after boresight alignment demonstrates the mitigation of the boresight systematic error impact on the final point cloud.
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How this classification was reachedexpand
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".