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 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 | 0.001 | 0.001 |
| 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".