2020/11/05 by Jinglun Feng, Liang Yang, Feng, Jinglun +9
Computer Science · Earth and Planetary Sciences · Engineering · #3D Surveying and Cultural Heritage #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Geophysical Methods and Applications #Image and Video Processing (eess.IV) #Microwave Imaging and Scattering Analysis #Robotics (cs.RO) #cs.CV #cs.RO #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2011.02635
Accepted by ICRA 2021
openalex publication_date 2020/11/05 · openalex created_date 2020/11/09 · arxiv created 2021/05/18 · arxiv updated 2021/05/19 · openalex updated_date 2026/07/28
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.