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Authors: Tobias Feigl 1 ; Andreas Porada 2 ; Steve Steiner 2 ; Christoffer Löffler 3 ; Christopher Mutschler 3 and Michael Philippsen 4

Affiliations: 1 Machine Learning and Information Fusion Group, Fraunhofer Institute for Integrated Circuits IIS, Nürnberg, Germany, Programming Systems Group, Friedrich-Alexander University (FAU), Erlangen-Nürnberg, Germany ; 2 Machine Learning and Information Fusion Group, Fraunhofer Institute for Integrated Circuits IIS, Nürnberg, Germany ; 3 Machine Learning and Information Fusion Group, Fraunhofer Institute for Integrated Circuits IIS, Nürnberg, Germany, Machine Learning and Data Analytics Lab, Friedrich-Alexander University (FAU), Erlangen-Nürnberg, Germany ; 4 Programming Systems Group, Friedrich-Alexander University (FAU), Erlangen-Nürnberg, Germany

ISBN: 978-989-758-402-2

Keyword(s): Augmented Reality (AR), Simultaneous Localization and Mapping (SLAM), Industry 4.0, Apple ARKit, Google ARCore, Microsoft Hololens.

Abstract: Augmented Reality (AR) systems are envisioned to soon be used as smart tools across many Industry 4.0 scenarios. The main promise is that such systems will make workers more productive when they can obtain additional situationally coordinated information both seemlessly and hands-free. This paper studies the applicability of today’s popular AR systems (Apple ARKit, Google ARCore, and Microsoft Hololens) in such an industrial context (large area of 1,600m2, long walking distances of 60m between cubicles, and dynamic environments with volatile natural features). With an elaborate measurement campaign that employs a sub-millimeter accurate optical localization system, we show that for such a context, i.e., when a reliable and accurate tracking of a user matters, the Simultaneous Localization and Mapping (SLAM) techniques of these AR systems are a showstopper. Out of the box, these AR systems are far from useful even for normal motion behavior. They accumulate an average error of about 17 m per 120m, with a scaling error of up to 14.4cm/m that is quasi-directly proportional to the path length. By adding natural features, the tracking reliability can be improved, but not enough. (More)

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Paper citation in several formats:
Feigl, T.; Porada, A.; Steiner, S.; Löffler, C.; Mutschler, C. and Philippsen, M. (2020). Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-scale Industry Environments.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, ISBN 978-989-758-402-2, pages 307-318. DOI: 10.5220/0008989903070318

@conference{grapp20,
author={Tobias Feigl. and Andreas Porada. and Steve Steiner. and Christoffer Löffler. and Christopher Mutschler. and Michael Philippsen.},
title={Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-scale Industry Environments},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP,},
year={2020},
pages={307-318},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008989903070318},
isbn={978-989-758-402-2},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP,
TI - Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-scale Industry Environments
SN - 978-989-758-402-2
AU - Feigl, T.
AU - Porada, A.
AU - Steiner, S.
AU - Löffler, C.
AU - Mutschler, C.
AU - Philippsen, M.
PY - 2020
SP - 307
EP - 318
DO - 10.5220/0008989903070318

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