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In numerous infrastructure health monitoring and inspection applications, swift and precise three-dimensional reconstruction of subsurface objects from ground penetrating radar (GPR) data is of critical importance, particularly given the recent advancements in perception modeling and the emergence of deep learning. Nonetheless, current research on the reconstruction of subsurface infrastructure scenes and objects faces limitations. Owing to the restrictions of conventional GPR data processing, these methodologies are prone to GPR data with noisy backgrounds and struggle to recreate noncylindrical objects. This paper investigates the back-projection (BP) approach for GPR-based three-dimensional (3D) subsurface target reconstruction and presents a learning model that formulates the reconstruction as an implicit BP from 2D to 3D representations, circumventing any preprocessing requirements in contrast to traditional techniques. The proposed learned model ultimately generates an explicit volumetric representation of the subsurface objects. Experimental results show at least a 33% enhancement in the performance of the proposed model compared to meticulously designed baselines. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a very useful nondestructive evaluation (NDE) device for locating and mapping underground assets prior to digging and trenching efforts in construction. This paper presents a novel robotic system to automate the GPR data-collection process, localize underground utilities, and interpret and reconstruct the underground objects for better visualization, allowing regular nonprofessional users to understand the survey results. This system is composed of three modules: (1) an omnidirectional robotic data-collection platform that carries a RGB-D camera with inertial measurement unit (IMU) and a GPR antenna to perform automatic GPR data collection and tag each GPR measurement with visual positioning information at every sampling step, (2) a learning-based migration module to interpret the raw GPR B-scan image into a two-dimensional (2D) cross-section model of objects, and (3) a three-dimensional (3D) reconstruction module, i.e., 30.0% GPRNet, to generate underground utility model represented as fine 3D point cloud. Comparative studies were performed on synthetic data and field GPR raw data with various incompleteness and noise. Experimental results demonstrated that our proposed method achieves a higher GPR imaging accuracy in mean intersection over union (IoU) than the conventional back-projection (BP) migration approach, and 6.9%−7.2% less loss in Chamfer distance (CD) than point cloud model reconstruction baseline methods. The GPR-based robotic inspection provides an effective tool for civil engineers to detect and survey underground utilities before construction. [ABSTRACT FROM AUTHOR]