CYLINDER-BASED SELF-CALIBRATION OF A PANORAMIC TERRESTRIAL LASER SCANNER
Bibliographic record
Abstract
Abstract. Terrestrial Laser Scanners (TLSs) have become state-of-the-art metrological sensors for many surveying purposes in recent years. Due to the demand for high precision surveying with TLSs, efficient, rigorous and in-situ calibration methodologies are always desired. Recent research on in-situ calibration with planar features has demonstrated improved cost-effectiveness and promising results (Glennie and Lichti, 2010; Chow et al., 2011; Chow et al., 2012). However, if there is a need for calibrating the scanners when sufficient plane surfaces with several orientations are not available, as commonly occurs indoors, other common geometric features, namely cylindrical structures, can be used as alternative geometric constraints for in-situ self-calibration. Cylindrical features can be found in indoor environments such as water pipes attached to the walls or suspended from ceilings, concrete pillars, metal poles and many others. In this paper, three 3D models of cylinders, with vertical and horizontal orientations containing one scaling, two rotational and two translational parameters are discussed. The cylinder models are parameterized with the sexternal orientation parameters and the additional parameters as the least-squares functional models for the self-calibration. The selfcalibration is examined with the real data obtained from the Lecia HDS6100 panoramic TLS. The results of vertical, horizontal and mixed cylinder-based calibration with data captured by different scanner position are analysed in detail in terms of the parameters correlations. The results show realistic estimation of calibration parameters for several cases. The results also suggest that using both vertical and horizontal cylinders for the calibration can effectively decorrelate the parameters especially for the case of lack of cylinder point cloud overlap. The concepts developed in this paper might also be extended to the hybrid type TLSs, as well as to the self-calibration of airborne laser scanning (ALS) using cylindrical features such as oil pipes or other large scale cylindrical infrastructure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".