  Ground Penetrating Radar (GPR) is a very useful non-destructive 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 the underground
utilities, interpret and reconstruct the underground objects for better
visualization allowing regular non-professional users to understand the survey
results. This system is composed of three modules: 1) an Omni-directional
robotic data collection platform, that carries an RGB-D camera with an 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 2D cross-section model of objects; 3) a 3D
reconstruction module, i.e., GPRNet, to generate underground utility model
represented as fine 3D point cloud. Comparative studies are performed on
synthetic data and field GPR raw data with various incompleteness and noise.
Experimental results demonstrate that our proposed method achieves a $30.0\%$
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 baseline methods regarding point cloud model
reconstruction. The GPR-based robotic inspection provides an effective tool for
civil engineers to detect and survey underground utilities before construction.
