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Localization limitations of ARCore, ARKit, and hololens in dynamic large-scale industry environments

: Feigl, T.; Porada, A.; Steiner, S.; Löffler, C.; Mutschler, C.; Philippsen, M.


Bouatouch, K.:
15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Proceedings. Vol.1: GRAPP : Valletta, Malta, Feb 27, 2020 - Feb 29, 2020
Setubal: SciTePress, 2020
ISBN: 978-989-758-402-2
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) <15, 2020, Valletta>
International Conference on Computer Graphics Theory and Applications (GRAPP) <2020, Valletta>
Conference Paper
Fraunhofer IIS ()

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 submillimeter 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 er ror of about 17m 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.