SLAM-centric visual inspection of civil infrastructure
Bibliographic record
Abstract
Existing robot-aided inspection methods suffer from inconsistent map accuracy, unreliable defect measurements, and platform-specific designs. This paper investigates whether a SLAM-centric framework can enable precise, repeatable, and platform-agnostic visual inspections. The framework integrates lidar–camera–inertial SLAM, offline trajectory refinement, inspection-map generation, defect extraction from imagery, and 3D ray-tracing to project defects into a unified map. The approach confirms that accurate defect localization, dimensional quantification, and dense inspection maps can be produced in real-world scenarios. This finding benefits infrastructure owners and inspectors by providing an end-to-end solution for robot-aided inspections that enable faster, safer, and more objective assessments compared to current qualitative workflows. The released datasets and software establish a foundation for future research on long-term defect monitoring and inspection automation. • Inspection-map generation pipeline using online SLAM, offline trajectory refinement, and decoupled map generation. • Precise and efficient image ray-tracing mapping pixels to unordered lidar point clouds without meshing or map alteration. • Platform-agnostic SLAM-centric inspection design enabling precise localization and high-quality inspection data. • First inspection-focused datasets with released software supporting future infrastructure-inspection research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".