Augmented Reality (AR) is a powerful tool for the visualisation of, and interaction with, digital information, and has been successfully deployed in a number of consumer applications. Despite this, AR has had limited success in industrial applications as the combined precision, accuracy, scalability and robustness of the systems are not up to industry standards. With these characteristics in mind, we present a concept Industrial AR (IAR) framework for use in outdoor environments.
Within this concept IAR framework, we focus on the improving the precision and accuracy of consumer level devices by focusing on the issue of localisation, utilising LiDAR based point clouds generated as part of normal surveying and engineering workflow.
We evaluate key design points to optimise the localisation solution, including the impact of increased field of view on feature matching performance, the filtering of feature matches between real imagery and an observed point cloud, and how pose can be estimated from 2D to 3D point correspondences.
The overall accuracy of this localisation algorithm with respect to ground-truth observations is determined, with unfiltered results indicating an on par horizontal accuracy and significantly improved vertical accuracy with best-case consumer GNSS solutions. When additional filtering is applied, results of localisation show a higher accuracy than best-case consumer GNSS.
| Identifer | oai:union.ndltd.org:canterbury.ac.nz/oai:ir.canterbury.ac.nz:10092/11052 |
| Date | January 2013 |
| Creators | Head-Mears, James Bradley |
| Publisher | University of Canterbury. HIT Lab NZ |
| Source Sets | University of Canterbury |
| Language | English |
| Detected Language | English |
| Type | Electronic thesis or dissertation, Text |
| Rights | Copyright James Bradley Head-Mears, http://library.canterbury.ac.nz/thesis/etheses_copyright.shtml |
| Relation | NZCU |
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