Defects such as surface cracks have considerably smaller scales in civil infrastructure, despite the grand scope of the structures themselves. This article introduces a robotic technique designed to bridge this scale gap by measuring the dimensions and characteristics of small-scale surface cracks in concrete structures at multiple scales. Using a convolutional neural network, our system initially detects potential surface cracks - regions of interests (ROI). Once identified, a high-definition laser scanner, steered by a robotic arm, scans the geometry of these ROIs. The detailed laser scan data is subsequently integrated with surrounding large-scale environmental scans obtained via LiDAR, utilizing 3D point cloud alignment methods. We validate the proposed solution through both computer simulations using the robotic operation system (ROS) as well as testing on a physical concrete specimen. Our method offers an unprecedented resolution of 0.004 mm for crack width measurement and has been successfully tested on real-world cracks with a width of 0.17 mm. A comparative analysis with existing vision-based solutions and traditional crack-width measuring instruments confirms the superior accuracy and efficiency of our multi-scale robotic approach in deriving crucial metrics necessary for assessing the structural health of civil infrastructure.
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