  Ground Penetrating Radar (GPR) is one of the most important non-destructive
evaluation (NDE) instruments to detect and locate underground objects (i.e.,
rebars, utility pipes). Many previous researches focus on GPR image-based
feature detection only, and none can process sparse GPR measurements to
successfully reconstruct a very fine and detailed 3D model of underground
objects for better visualization. To address this problem, this paper presents
a novel robotic system to collect GPR data, localize the underground utilities,
and reconstruct the underground objects' dense point cloud model. This system
is composed of three modules: 1) visual-inertial-based GPR data collection
module, which tags the GPR measurements with positioning information provided
by an omnidirectional robot; 2) a deep neural network (DNN) migration module to
interpret the raw GPR B-scan image into a cross-section of object model; 3) a
DNN-based 3D reconstruction module, i.e., GPRNet, to generate underground
utility model with the fine 3D point cloud. In this paper, both the
quantitative and qualitative experiment results verify our method that can
generate a dense and complete point cloud model of pipe-shaped utilities based
on a sparse input, i.e., GPR raw data incompleteness and various noise. The
experiment results on synthetic data and field test data further support the
effectiveness of our approach.
