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 (2020)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2020

Publisher: SciTePress

City/Town: Portugal

Pages Range: 307-318

Conference Proceedings Title: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP

Event location: Valletta MT

ISBN: 978-989-758-402-2

URI: http://www.grapp.visigrapp.org/

DOI: 10.5220/0008989903070318

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 1,600m 2 , 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 error 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.

Authors with CRIS profile

Tobias Feigl Lehrstuhl für Informatik 2 (Programmiersysteme) Christoffer Löffler Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Christopher Mutschler Lehrstuhl für Informatik 2 (Programmiersysteme) Michael Philippsen Lehrstuhl für Informatik 2 (Programmiersysteme)

Additional Organisation(s)

Machine Learning and Data Analytics Lab

Related research project(s)

Recurrent Neuronal Networks (RNNs) for Real-Time Estimation of Nonlinear Motion Models (RuNN) Oct. 1, 2017 - March 31, 2021

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Feigl, T., Porada, A., Steiner, S., Löffler, C., Mutschler, C., & Philippsen, M. (2020). Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-Scale Industry Environments. In Kadi Bouatouch, A. Augusto Sousa, Jose Braz (Eds.), Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP (pp. 307-318). Valletta, MT: Portugal: SciTePress.

MLA:

Feigl, Tobias, et al. "Localization Limitations of ARCore, ARKit, and Hololens in Dynamic Large-Scale Industry Environments." Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (GRAPP 2020), Valletta Ed. Kadi Bouatouch, A. Augusto Sousa, Jose Braz, Portugal: SciTePress, 2020. 307-318.

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