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Open Access Highly Accessed Research article

High-precision vision-based mobile augmented reality system for context-aware architectural, engineering, construction and facility management (AEC/FM) applications

Hyojoon Bae1*, Mani Golparvar-Fard2 and Jules White3

Author Affiliations

1 Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

2 Department of Civil and Environmental Engineering, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

3 Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA

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Visualization in Engineering 2013, 1:3  doi:10.1186/2213-7459-1-3

Published: 13 June 2013

Abstract

Background

Many context-aware techniques have been proposed to deliver cyber-information, such as project specifications or drawings, to on-site users by intelligently interpreting their environment. However, these techniques primarily rely on RF-based location tracking technologies (e.g., GPS or WLAN), which typically do not provide sufficient precision in congested construction sites or require additional hardware and custom mobile devices.

Method

This paper presents a new vision-based mobile augmented reality system that allows field personnel to query and access 3D cyber-information on-site by using photographs taken from standard mobile devices. The system does not require any location tracking modules, external hardware attachments, and/or optical fiducial markers for localizing a user’s position. Rather, the user’s location and orientation are purely derived by comparing images from the user’s mobile device to a 3D point cloud model generated from a set of pre-collected site photographs.

Results

The experimental results show that 1) the underlying 3D reconstruction module of the system generates complete 3D point cloud models of target scene, and is up to 35 times faster than other state-of-the-art Structure-from-Motion (SfM) algorithms, 2) the localization time takes at most few seconds in actual construction site.

Conclusion

The localization speed and empirical accuracy of the system provides the ability to use the system on real-world construction sites. Using an actual construction case study, the perceived benefits and limitations of the proposed method for on-site context-aware applications are discussed in detail.