TY - GEN
T1 - Distributed Edge Computing for Cooperative Augmented Reality
T2 - Sensors and Systems for Space Applications XVII 2024
AU - Cheng, Cheng Yu
AU - Zhao, Qi
AU - Wu, Cheng Ying
AU - Yang, Yuchen
AU - Qureshi, Muhammad A.
AU - Liu, Hang
AU - Chen, Genshe
N1 - Publisher Copyright:
© 2024 SPIE.
PY - 2024
Y1 - 2024
N2 - Cooperative Augmented Reality (AR) can provide real-time, immersive, and context-aware situational awareness while enhancing mobile sensing capabilities and benefiting various applications. Distributed edge computing has emerged as an essential paradigm to facilitate cooperative AR. We designed and implemented a distributed system to enable fast, reliable, and scalable cooperative AR. In this paper, we present a novel approach and architecture that integrates advanced sensing, communications, and processing techniques to create such a cooperative AR system, and demonstrate its capability with HoloLens and edge servers connected over a wireless network. Our research addresses the challenges of implementing a distributed cooperative AR system capable of capturing data from a multitude of sensors on HoloLens, performing fusion and accurate object recognition, and seamlessly projecting the reconstructed 3D model into the wearer's field of view. The paper delves into the intricate architecture of the proposed cooperative AR system, detailing its distributed sensing and edge computing components, and the Apache Storm-integrated platform. The implementation encompasses data collection, aggregation, analysis, object recognition, and rendering of 3D models on the HoloLens, all in real-time. The proposed system enhances the AR experience while showcasing the vast potential of distributed edge computing. Our findings illustrate the feasibility and advantages of merging distributed cooperative sensing and edge computing to offer dynamic, immersive AR experiences, paving the way for new applications.
AB - Cooperative Augmented Reality (AR) can provide real-time, immersive, and context-aware situational awareness while enhancing mobile sensing capabilities and benefiting various applications. Distributed edge computing has emerged as an essential paradigm to facilitate cooperative AR. We designed and implemented a distributed system to enable fast, reliable, and scalable cooperative AR. In this paper, we present a novel approach and architecture that integrates advanced sensing, communications, and processing techniques to create such a cooperative AR system, and demonstrate its capability with HoloLens and edge servers connected over a wireless network. Our research addresses the challenges of implementing a distributed cooperative AR system capable of capturing data from a multitude of sensors on HoloLens, performing fusion and accurate object recognition, and seamlessly projecting the reconstructed 3D model into the wearer's field of view. The paper delves into the intricate architecture of the proposed cooperative AR system, detailing its distributed sensing and edge computing components, and the Apache Storm-integrated platform. The implementation encompasses data collection, aggregation, analysis, object recognition, and rendering of 3D models on the HoloLens, all in real-time. The proposed system enhances the AR experience while showcasing the vast potential of distributed edge computing. Our findings illustrate the feasibility and advantages of merging distributed cooperative sensing and edge computing to offer dynamic, immersive AR experiences, paving the way for new applications.
KW - Apache Storm
KW - Augmented reality
KW - Cooperative sensing
KW - Distributed edge computing
KW - HoloLens
KW - Mobile sensing
UR - https://www.scopus.com/pages/publications/85196501841
U2 - 10.1117/12.3021841
DO - 10.1117/12.3021841
M3 - Conference contribution
AN - SCOPUS:85196501841
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Sensors and Systems for Space Applications XVII
A2 - Chen, Genshe
A2 - Pham, Khanh D.
PB - SPIE
Y2 - 23 April 2024 through 25 April 2024
ER -