Robotic Inspection and Subsurface Defect Mapping Using Impact-Echo and Ground Penetrating Radar

Concrete infrastructure often develops a variety of internal flaws that cannot be detected through visual inspection alone, and must be regularly inspected with other methods to maintain structural integrity. It has been demonstrated through previous studies that relying solely on a single non-destructive evaluation (NDE) method can be insufficient in providing a comprehensive evaluation of the structure's condition. In addition, manual NDE data collection can be labor-intensive for on-site engineers. This paper presents a robotic inspection system that uses vision-based positioning and tags NDE measurement with pose information to reveal and map subsurface defects. The system consists of three modules: 1) an Omni-directional robotic data collection platform equipped with a Realsense D435i camera for localization, an impact-echo (IE) sensor, and a ground penetrating radar (GPR), to perform automatic NDE data collection; 2) an IE data processing module that utilizes both learning-based and classical methods to interpret the IE data and reveal subsurface objects; 3) a GPR data processing module to reconstruct underground targets and create a 3D map for better visualization. Field testing demonstrates that the robotic system significantly increases the data collection speed, and the correlation of findings from both IE and GPR sensors give a comprehensive evaluation of concrete structures that will benefit the inspection and maintenance industry of civil infrastructure.