Robust pervasive context-aware augmented reality (AR) has the potential to enable a range of applications that support users in reaching their personal and professional goals. In such applications, AR can be used to deliver richer, more immersive, and more timely just in time adaptive interventions (JITAI) than conventional mobile solutions, leading to more effective support of the user. This position paper defines a research agenda centered on improving AR applications' environmental, user, and social context awareness. Specifically, we argue for two key architectural approaches that will allow pushing AR context awareness to the next level: use of wearable and Internet of Things (IoT) devices as additional data streams that complement the data captured by the AR devices, and the development of edge computing-based mechanisms for enriching existing scene understanding and simultaneous localization and mapping (SLAM) algorithms. The paper outlines a collection of specific research directions in the development of such architectures and in the design of next-generation environmental, user, and social context awareness algorithms.
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Scenariot: Spatially Mapping Smart Things Within Augmented Reality Scenes
The emerging simultaneous localizing and mapping (SLAM)
based tracking technique allows the mobile AR device spatial
awareness of the physical world. Still, smart things are not
fully supported with the spatial awareness in AR. Therefore,
we present Scenariot, a method that enables instant discovery
and localization of the surrounding smart things while
also spatially registering them with a SLAM based mobile
AR system. By exploiting the spatial relationships between
mobile AR systems and smart things, Scenariot fosters in-situ
interactions with connected devices. We embed Ultra-Wide
Band (UWB) RF units into the AR device and the controllers
of the smart things, which allows for measuring the distances
between them. With a one-time initial calibration, users localize
multiple IoT devices and map them within the AR scenes. Through a series of experiments and evaluations, we validate
the localization accuracy as well as the performance of the
enabled spatial aware interactions. Further, we demonstrate
various use cases through Scenariot.
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- Award ID(s):
- 1632154
- NSF-PAR ID:
- 10112940
- Date Published:
- Journal Name:
- Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 219
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3173574.3173793
