Abstract

Abstract. Ground-penetrating 2D radar scans are captured in road environments for examination of pavement condition and below-ground variations such as lowerings and developing pot-holes. 3D point clouds captured above ground provide a precise digital representation of the road’s surface and the surrounding environment. If both data sources are captured for the same area, a combined visualization is a valuable tool for infrastructure maintenance tasks. This paper presents visualization techniques developed for the combined visual exploration of the data captured in road environments. Main challenges are the positioning of the ground radar data within the 3D environment and the reduction of occlusion for individual data sets. By projecting the measured ground radar data onto the precise trajectory of the scan, it can be displayed within the context of the 3D point cloud representation of the road environment. We show that customizable overlay, filtering, and cropping techniques enable insightful data exploration. A 3D renderer combines both data sources. To enable an inspection of areas of interest, ground radar data can be elevated above ground level for better visibility. An interactive lens approach enables to visualize data sources that are currently occluded by others. The visualization techniques prove to be a valuable tool for ground layer anomaly inspection and were evaluated in a real-world data set. The combination of 2D ground radar scans with 3D point cloud data improves data interpretation by giving context information (e.g., about manholes in the street) that can be directly accessed during evaluation.

Highlights

  • Mobile mapping techniques have been established in various environments (Li, 1997) and become more and more common for infrastructure maintenance purposes (Li et al, 2017)

  • Based on those data characteristics, we have identified the following requirements that need to be addressed for a combined visualization of 3D point clouds and Ground penetrating radar (GPR) data: R1 No limitations regarding used acquisition methods, as well as the number, scale and size of the data sets

  • We opted against using Level of detail (LOD) representations, since the size of the GPR data sets evaluated in the context of this paper was neglectable by comparison (i. e., 25.7 MB raw data per B-scan)

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Summary

INTRODUCTION

Mobile mapping techniques have been established in various environments (Li, 1997) and become more and more common for infrastructure maintenance purposes (Li et al, 2017). Municipalities and private organizations are capturing geospatial data with multi-scanner systems, enabling detailed analysis of urban environments, large facilities, and infrastructure networks (Airfield Inventory, 2018) Remote sensing equipment, such as LiDAR scanners, can be mounted on top of arbitrary cars and enables high precision scans of road surfaces, curbstones, and road space assets (Jaakkola et al, 2008). Ground penetrating radar scanners can be mounted onto scanning vehicles, adding an additional data source for the region not accessible by LiDAR scanning (Mobile GPR, 2017). By adding road surface information from the 3D point cloud, false positives like manholes can be distinguished from other anomalies in the road’s subsoil

RELATED WORK
DATA CHARACTERISTICS AND REQUIREMENTS
SYSTEM OVERVIEW
Ground Penetrating Radar Manager
Processing Engine
Rendering Engine
Interaction Handler
EVALUATION
Usability
Performance
CONCLUSIONS AND FUTURE WORK
Full Text
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