Development of a Laboratory for Testing the Accuracy of Terrestrial 3D Laser Scanning Technologies
Bibliographic record
Abstract
The mining market is currently overwhelmed by technology vendors offering scanning equipment as ‘solutions’ for real time mapping and monitoring rock mass movement for mine safety. Mines are left with a problem in that the technology is mostly unproven and not originally designed for mine safety accuracies. Scanning system accuracy assessment needs to be done so as to increase the level of confidence and trust in the quality of the results. The scope of this research is set a laboratory for testing terrestrial laser scanning (TLS) systems – complete with targets fix on the wall of the testing laboratory, which plays a vital role in creating high quality and reliable digital point clouds. To improve the accuracy test of the scanning system, we support exact positioning and distance measurement of points cloud by providing revolutionizing surveying solutions and infrastructure development. The FARO, a static 3D laser scanner and uGPS, a mobile 3D laser scanning system are tested in this research. If the level of accuracy of these TLS systems can be ascertained, this can fit into the production process, ore flow analysis to measure discrepancy and metal accounting principles. Notably, this will add value to mining operations chains through measurement and adequate monitoring of process by revealing the modifying factor contributing to mine loss. More importantly good decisions can be made on mine evacuation when point cloud comparisons raise alarm on rock mass movement. With this laboratory, we can offer a vital service to the mining industry by certifying new scanning solutions as these arrive on the market. This will make mines safer.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".