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Evaluation of HoloLens Tracking and Depth Sensing for Indoor Mapping Applications

Hübner, Patrick; Clintworth, Kate; Liu, Qingyi; Weinmann, Martin; Wursthorn, Sven

The Microsoft HoloLens is a head-worn mobile augmented reality device that is capable of mapping its direct environment in real-time as triangle meshes and localize itself within these three-dimensional meshes simultaneously. The device is equipped with a variety of sensors including four tracking cameras and a time-of-flight (ToF) range camera. Sensor images and their poses estimated by the built-in tracking system can be accessed by the user. This makes the HoloLens potentially interesting as an indoor mapping device. In this paper, we introduce the different sensors of the device and evaluate the complete system in respect of the task of mapping indoor environments. The overall quality of such a system depends mainly on the quality of the depth sensor together with its associated pose derived from the tracking system. For this purpose, we first evaluate the performance of the HoloLens depth sensor and its tracking system separately. Finally, we evaluate the overall system regarding its capability for mapping multi-room environments.

Verlagsausgabe §
DOI: 10.5445/IR/1000105959
Veröffentlicht am 20.02.2020
DOI: 10.3390/s20041021
Zitationen: 23
Web of Science
Zitationen: 17
Zitationen: 41
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1424-8220
KITopen-ID: 1000105959
Erschienen in Sensors
Verlag MDPI
Band 20
Heft 4
Seiten 1021
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Gefördert vom Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (MWK) im Rahmen des Open-Access-Förderprogramms "BW BigDIWA"
Vorab online veröffentlicht am 14.02.2020
Schlagwörter indoor mapping; augmented reality; HoloLens; time-of-flight camera; depth camera; tracking
Nachgewiesen in Web of Science
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