SLAM-centric visual inspection of civil infrastructure

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.