Advancing Geometric Inspection of Non-Rigid Structures Using Mobile Robots and 3D Laser Scanners
Bibliographic record
Abstract
Abstract This paper introduces a novel approach for inspecting non-rigid structures, which are difficult to assess using traditional methods due to their flexibility and susceptibility to deformation. Our method enables comprehensive geometric inspection by integrating mobile robotics and 3D laser scanning. The system autonomously navigates around the structure, performing high-resolution scans with real-time adjustment of scanning parameters for optimal coverage. Non-rigid registration techniques align the acquired data with the structure’s CAD model, enhancing defect identification accuracy. Geometric defects are quantified through comparative analysis, providing insights into structural health. This proactive approach helps prevent costly damages and mitigate risks, offering advantages over traditional methods such as enhanced efficiency and safety. Incorporating real-time monitoring into maintenance schedules optimizes asset performance and extends operational lifespan, which is particularly beneficial for industries like aeronautics and automotive, where inspection is mandatory for ensuring part and structure conformity to design requirements.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".