mlsanityData to 14 Oct 2026

Robotic Inspection of Underground Utilities for Construction Survey Using a Ground Penetrating Radar

arXiv 2106.01907 ↗First posted 3 Jun 2021eess.IV cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 51 references that are in the map. First posted on arXiv 3 Jun 2021.

Builds on

11 of 51 references.

References · 11

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  4. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  5. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 979
  6. 2020ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAMcited by 878
  7. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  8. 2018PCN: Point Completion Networkcited by 384
  9. 2020PF-Net: Point Fractal Network for 3D Point Cloud Completioncited by 146
  10. 2015Loss Functions for Neural Networks for Image Processingcited by 41
  11. 2020GPR-based Model Reconstruction System for Underground Utilities Using GPRNetcited by 1

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.