Utilization of Augmented Reality Technique for Sewer Condition Visualization

Abstract

Wastewater pipelines are largely buried underground, and techniques for assessing and visualizing their condition are critical for planning and rehabilitation. This paper introduces a framework for integrating Geographic Information System (GIS), 3D-creation platform, augmented reality (AR) techniques, and machine learning algorithms for the dynamic visualization of the condition of sewer networks. A sewer network in Ålesund City, Norway, was used as a case study, and the developed framework was implemented on an Android OS and Microsoft HoloLens. The results show the potential applications of the integrated framework of GIS, AR, and 3D models for sewer condition visualization. The positioning accuracy of the application for 2D objects is equivalent to that of well-designed GPS receivers (approximately 1–3 m), depending on the handheld device used. Loading and locating 3D objects will be limited by the performance of the devices used.