Towards Industrial IoT-AR Systems using Deep Learning-Based Object Pose Estimation
Name
iot-ar.pdf
Description
Accepted version
Size
4.84 MB
Format
Adobe PDF
Checksum (MD5)
62b882e5105d30364b47e173b0a3cdac
Author(s)
Sun, Yongbin
Kantareddy, Sai Nithin R.
Siegel, Joshua
Armengol Urpi, Alexandre
Wu, Xiaoyu
Wang, Hongyu
Sarma, Sanjay
Date Issued
January 2020
Journal
38th International Performance Computing and Communications Conference
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Sun, Yongbin et al. "Towards Industrial IoT-AR Systems using Deep Learning-Based Object Pose Estimation." 38th International Performance Computing and Communications Conference, October 2019, London, United Kingdom, United Kingdom, Institute of Electrical and Electronics Engineers, January 2020. © 2019 IEEE
Version
Author's final manuscript
Abstract
Augmented Reality (AR) is known to enhance user experience, however, it remains under-adopted in industry. We present an AR interaction system improving human-machine coordination in Internet of Things (IoT) and Industry 4.0 applications including manufacturing and assembly, maintenance and safety, and other highly-interactive functions. A driver of slow adoption is the computational complexity and inaccuracy in localization and rendering digital content. AR systems may render digital content close to the associated physical objects, but traditional object recognition and localization modules perform poorly when tracking texture-less objects and complex shapes, presenting a need for robust and efficient digital content rendering techniques. We propose a method of improving IoT-AR by integrating Deep Learning with AR to increase accuracy and robustness of the target object localization module, taking both color and depth images as input and outputting the target's pose parameters. Quantitative and qualitative experiments prove this system's efficacy and show potential for fusing these emerging technologies in real-world applications.
MIT Department
Massachusetts Institute of Technology. Auto-ID Laboratory
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ipccc47392.2019.8958753
