Interactive Visualization of 3D Coordinate Uncertainties in Terrestrial Laser-Scanning Point Clouds Using OpenGL Shader Language
Publication: Journal of Surveying Engineering
Volume 145, Issue 1
Abstract
Although laser-scanning total propagated uncertainty (TPU) is an active topic in the research community, there is a current dearth of commercial or open-source software for generating point clouds with per-point, three-dimensional (3D) coordinate uncertainties from terrestrial, mobile, and airborne laser scanning. Consequentially, there is a corresponding lack of tools for on-the-fly 3D visualization of these point cloud uncertainties. Visualization tools could be incredibly valuable for analyzing and communicating the spatial variability of uncertainty in a data set, ultimately enhancing both qualitative and quantitative analyses. This study presents an efficient visualization framework for terrestrial laser-scanning (TLS) point cloud uncertainty calculation utilizing the OpenGL Shader Language (GLSL). The methods were tested on four data sets, ranging from a controlled indoor scene containing objects representing simple geometric shapes (e.g., spheres and cones) to a complex forest environment with many types of natural objects. OpenGL shader-based uncertainty visualization was found to aid with the assessment of the effects of modifying models of measurement uncertainty, such as accounting for the nonlinear increase in laser beam radius at short ranges when modeling laser beamwidth-derived uncertainty, as well as range- and angular-based component uncertainties.
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Acknowledgments
Funding for acquisition of the different TLS data sets was provided in part by National Science Foundation (NSF) through Award 1417603 and the Geotechnical Extreme Events Reconnaissance (GEER) Association (NSF Awards CMMI-1266418 and CMMI-1724866). Funding for the development of the shader-based point cloud uncertainty calculation and visualization solution was partially provided by the NSF through Award CMMI-1351487. Preston Hartzell (University of Houston) provided clarifications of his excellent work in TLS point cloud uncertainty propagation. Chris Foster developed the Displaz point cloud visualization software, provided guidance with shader development, and shared a fragment shader that visualizes normal vectors. Erzhuo Che [Oregon State University (OSU)] assisted with the acquisition and processing of the TLS data for Scan A and Scan B and provided useful input along with Richard Slocum (OSU) during development of the point cloud uncertainty calculation and visualization solution. Martha McAlister (OSU), Hamid Mahmoudabadi (OSU), and Patrick Burns (OSU) assisted with the acquisition of Scan D. Leica Geosystems and David Evans and Associates (Portland, Oregon) provided software and/or hardware used in this study.
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Published In
Journal of Surveying Engineering
Volume 145 • Issue 1 • February 2019
Copyright
© 2018 American Society of Civil Engineers.
History
Received: Jan 24, 2018
Accepted: Jun 20, 2018
Published online: Nov 1, 2018
Published in print: Feb 1, 2019
Discussion open until: Apr 1, 2019
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