Improvements to and Comparison of Static Terrestrial LiDAR Self-Calibration Methods
Bibliographic record
Abstract
Terrestrial laser scanners are sophisticated instruments that operate much like high-speed total stations. It has previously been shown that unmodelled systematic errors can exist in modern terrestrial laser scanners that deteriorate their geometric measurement precision and accuracy. Typically, signalised targets are used in point-based self-calibrations to identify and model the systematic errors. Although this method has proven its effectiveness, a large quantity of signalised targets is required and is therefore labour-intensive and limits its practicality. In recent years, feature-based self-calibration of aerial, mobile terrestrial, and static terrestrial laser scanning systems has been demonstrated. In this paper, the commonalities and differences between point-based and plane-based self-calibration (in terms of model identification and parameter correlation) are explored. The results of this research indicate that much of the knowledge from point-based self-calibration can be directly transferred to plane-based calibration and that the two calibration approaches are nearly equivalent. New network configurations, such as the inclusion of tilted scans, were also studied and prove to be an effective means for strengthening the self-calibration solution, and improved recoverability of the horizontal collimation axis error for hybrid scanners, which has always posed a challenge in the past.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".