skip to main content
10.1145/3173574.3173793acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Scenariot: Spatially Mapping Smart Things Within Augmented Reality Scenes

Published:21 April 2018Publication History

ABSTRACT

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.

Skip Supplemental Material Section

Supplemental Material

pn2347-file5.mp4

mp4

12.9 MB

pn2347.mp4

mp4

58.4 MB

References

  1. Abdulrahman Alarifi, AbdulMalik Al-Salman, Mansour Alsaleh, Ahmad Alnafessah, Suheer Al-Hadhrami, Mai A Al-Ammar, and Hend S Al-Khalifa. 2016. Ultra wideband indoor positioning technologies: Analysis and recent advances. Sensors 16, 5 (2016), 707.Google ScholarGoogle ScholarCross RefCross Ref
  2. Isaac Amundson and Xenofon Koutsoukos. 2009. A survey on localization for mobile wireless sensor networks. Mobile entity localization and tracking in GPS-less environnments (2009), 235--254. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Ferran Argelaguet and Carlos Andujar. 2009. Visual feedback techniques for virtual pointing on stereoscopic displays. In Proceedings of the 16th ACM Symposium on Virtual Reality Software and Technology. ACM, 163--170. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. ASUS. 2017. ZenFone-AR. (2017). Retrieved September 1, 2017 from = https://www.asus.com/us/Phone/ZenFoneAR-ZS571KL/.Google ScholarGoogle Scholar
  5. Shuanghua Bai and Houduo Qi. 2016. Tackling the flip ambiguity in wireless sensor network localization and beyond. Digital Signal Processing 55 (2016), 85--97. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Till Ballendat, Nicolai Marquardt, and Saul Greenberg. 2010. Proxemic interaction: designing for a proximity and orientation-aware environment. In ACM International Conference on Interactive Tabletops and Surfaces. ACM, 121--130. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Ingwer Borg and Patrick JF Groenen. 2005. Modern multidimensional scaling: Theory and applications. Springer Science & Business Media.Google ScholarGoogle Scholar
  8. Sebastian Boring, Dominikus Baur, Andreas Butz, Sean Gustafson, and Patrick Baudisch. 2010. Touch projector: mobile interaction through video. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2287--2296. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Barry Brumitt, John Krumm, Brian Meyers, and Steven Shafer. 2000. Ubiquitous computing and the role of geometry. IEEE Personal Communications 7, 5 (2000), 41--43.Google ScholarGoogle ScholarCross RefCross Ref
  10. Yu-Hsiang Chen, Ben Zhang, Claire Tuna, Yang Li, Edward A Lee, and Bjorn Hartmann. 2013. A context menu for the real world: Controlling physical appliances through head-worn infrared targeting. Technical Report. CALIFORNIA UNIV BERKELEY DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES.Google ScholarGoogle Scholar
  11. Jose A Costa, Neal Patwari, and Alfred O Hero III. 2006. Distributed weighted-multidimensional scaling for node localization in sensor networks. ACM Transactions on Sensor Networks (TOSN) 2, 1 (2006), 39--64. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Adrian A de Freitas, Michael Nebeling, Xiang'Anthony' Chen, Junrui Yang, Akshaye Shreenithi Kirupa Karthikeyan Ranithangam, and Anind K Dey. 2016. Snap-to-it: A user-inspired platform for opportunistic device interactions. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 5909--5920. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Jan De Leeuw and Patrick Mair. 2011. Multidimensional scaling using majorization: SMACOF in R. Department of Statistics, UCLA (2011).Google ScholarGoogle Scholar
  14. Carmelo Di Franco, Enrico Bini, Mauro Marinoni, and Giorgio C Buttazzo. 2017a. Multidimensional scaling localization with anchors. In Autonomous Robot Systems and Competitions (ICARSC), 2017 IEEE International Conference on. IEEE, 49--54.Google ScholarGoogle ScholarCross RefCross Ref
  15. Carmelo Di Franco, Amanda Prorok, Nikolay Atanasov, Benjamin P Kempke, Prabal Dutta, Vijay Kumar, and George J Pappas. 2017b. Calibration-free network localization using non-line-of-sight ultra-wideband measurements.. In IPSN. 235--246. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Ivan Dokmanic, Reza Parhizkar, Juri Ranieri, and Martin Vetterli. 2015. Euclidean distance matrices: Essential theory, algorithms, and applications. IEEE Signal Processing Magazine 32, 6 (2015), 12--30.Google ScholarGoogle ScholarCross RefCross Ref
  17. Hans Gellersen, Carl Fischer, Dominique Guinard, Roswitha Gostner, Gerd Kortuem, Christian Kray, Enrico Rukzio, and Sara Streng. 2009. Supporting device discovery and spontaneous interaction with spatial references. Personal and Ubiquitous Computing 13, 4 (2009), 255--264. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Google. 2017. ARCore. (2017). Retrieved September 1, 2017 from = https://developers.google.com/ar/.Google ScholarGoogle Scholar
  19. Uwe Gruenefeld, Abdallah El Ali, Wilko Heuten, and Susanne Boll. 2017. Visualizing out-of-view objects in head-mounted augmented reality. In Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 81. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Anhong Guo, Jeeeun Kim, Xiang'Anthony' Chen, Tom Yeh, Scott E Hudson, Jennifer Mankoff, and Jeffrey P Bigham. 2017. Facade: Auto-generating tactile interfaces to appliances. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 5826--5838. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Dirk Hahnel, Wolfram Burgard, Dieter Fox, Ken Fishkin, and Matthai Philipose. 2004. Mapping and localization with RFID technology. In Robotics and Automation, 2004. Proceedings. ICRA'04. 2004 IEEE International Conference on, Vol. 1. IEEE, 1015--1020.Google ScholarGoogle ScholarCross RefCross Ref
  22. Valentin Heun, James Hobin, and Pattie Maes. 2013. Reality editor: programming smarter objects. In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication. ACM, 307--310. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Yi Jiang and Victor CM Leung. 2007. An asymmetric double sided two-way ranging for crystal offset. In Signals, Systems and Electronics, 2007. ISSSE'07. International Symposium on. IEEE, 525--528.Google ScholarGoogle ScholarCross RefCross Ref
  24. Gierad Laput, Chouchang Yang, Robert Xiao, Alanson Sample, and Chris Harrison. 2015. Em-sense: Touch recognition of uninstrumented, electrical and electromechanical objects. In Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology. ACM, 157--166. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. David Ledo, Saul Greenberg, Nicolai Marquardt, and Sebastian Boring. 2015. Proxemic-aware controls: Designing remote controls for ubiquitous computing ecologies. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, 187--198. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Sikun Lin, Hao Fei Cheng, Weikai Li, Zhanpeng Huang, Pan Hui, and Christoph Peylo. 2017a. Ubii: Physical World Interaction Through Augmented Reality. IEEE Transactions on Mobile Computing 16, 3 (2017), 872--885. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Yung-Ta Lin, Yi-Chi Liao, Shan-Yuan Teng, Yi-Ju Chung, Liwei Chan, and Bing-Yu Chen. 2017b. Outside-In: Visualizing Out-of-Sight Regions-of-Interest in a 360 Video Using Spatial Picture-in-Picture Previews. In Proceedings of the 30th Annual Symposium on User Interface Software and Technology. ACM, --. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Simon Mayer, Markus Schalch, Marian George, and Gábor Sörös. 2013. Device recognition for intuitive interaction with the web of things. In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication. ACM, 239--242. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Microsoft. 2017. Hololens. (2017). Retrieved September 1, 2017 from = https://www.microsoft.com/en-us/hololens.Google ScholarGoogle Scholar
  30. Gang Pan, Jiahui Wu, Daqing Zhang, Zhaohui Wu, Yingchun Yang, and Shijian Li. 2010. GeeAir: a universal multimodal remote control device for home appliances. Personal and Ubiquitous Computing 14, 8 (2010), 723--735. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Charith Perera, Arkady Zaslavsky, Peter Christen, and Dimitrios Georgakopoulos. 2014. Context aware computing for the internet of things: A survey. IEEE Communications Surveys & Tutorials 16, 1 (2014), 414--454.Google ScholarGoogle ScholarCross RefCross Ref
  32. Sudeep Pillai and John Leonard. 2015. Monocular slam supported object recognition. arXiv preprint arXiv:1506.01732 (2015).Google ScholarGoogle Scholar
  33. Math.Net Project. 2017. Math.Net Numerics. (2017). Retrieved September 1, 2017 from = https://numerics.mathdotnet.com/.Google ScholarGoogle Scholar
  34. Kun Qian, Chenshu Wu, Zimu Zhou, Yue Zheng, Zheng Yang, and Yunhao Liu. 2017. Inferring motion direction using commodity wi-fi for interactive exergames. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 1961--1972. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Jun Rekimoto, Yuji Ayatsuka, Michimune Kohno, and Haruo Oba. 2003. Proximal Interactions: A Direct Manipulation Technique for Wireless Networking.. In Interact, Vol. 3. 511--518.Google ScholarGoogle Scholar
  36. Enrico Rukzio, Karin Leichtenstern, Vic Callaghan, Paul Holleis, Albrecht Schmidt, and Jeannette Chin. 2006. An experimental comparison of physical mobile interaction techniques: Touching, pointing and scanning. UbiComp 2006: Ubiquitous Computing (2006), 87--104. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Spencer Russell, Gershon Dublon, and Joseph A Paradiso. 2016. Hearthere: Networked sensory prosthetics through auditory augmented reality. In Proceedings of the 7th Augmented Human International Conference 2016. ACM, 20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Renato F Salas-Moreno, Richard A Newcombe, Hauke Strasdat, Paul HJ Kelly, and Andrew J Davison. 2013. Slam++: Simultaneous localisation and mapping at the level of objects. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1352--1359. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Dominik Schmidt, David Molyneaux, and Xiang Cao. 2012. PICOntrol: using a handheld projector for direct control of physical devices through visible light. In Proceedings of the 25th annual ACM symposium on User interface software and technology. ACM, 379--388. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Eldon Schoop, Michelle Nguyen, Daniel Lim, Valkyrie Savage, Sean Follmer, and Björn Hartmann. 2016. Drill Sergeant: Supporting physical construction projects through an ecosystem of augmented tools. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 1607--1614. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Unity3D. 2017. Unity3D. (2017). Retrieved September 1, 2017 from = https://unity3d.com/.Google ScholarGoogle Scholar
  42. Pasi Välkkynen and Timo Tuomisto. 2005. Physical Browsing Research. PERMID 2005 (2005), 35--38.Google ScholarGoogle Scholar
  43. S Venkatesh and RM Buehrer. 2007. Non-line-of-sight identification in ultra-wideband systems based on received signal statistics. IET Microwaves, Antennas & Propagation 1, 6 (2007), 1120--1130.Google ScholarGoogle ScholarCross RefCross Ref
  44. Edward J Wang, Tien-Jui Lee, Alex Mariakakis, Mayank Goel, Sidhant Gupta, and Shwetak N Patel. 2015. Magnifisense: Inferring device interaction using wrist-worn passive magneto-inductive sensors. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 15--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Roy Want, Kenneth P Fishkin, Anuj Gujar, and Beverly L Harrison. 1999. Bridging physical and virtual worlds with electronic tags. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems. ACM, 370--377. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. wikitude. 2017. wikitude. (2017). Retrieved September 1, 2017 from = https://www.wikitude.com/.Google ScholarGoogle Scholar
  47. Robert Xiao, Gierad Laput, Yang Zhang, and Chris Harrison. 2017. Deus EM Machina: On-Touch Contextual Functionality for Smart IoT Appliances. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 4000--4008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Sang Ho Yoon, Yunbo Zhang, Ke Huo, and Karthik Ramani. 2016. TRing: Instant and Customizable Interactions with Objects Using an Embedded Magnet and a Finger-Worn Device. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology. ACM, 169--181. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Scenariot: Spatially Mapping Smart Things Within Augmented Reality Scenes

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
        April 2018
        8489 pages
        ISBN:9781450356206
        DOI:10.1145/3173574

        Copyright © 2018 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 21 April 2018

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        CHI '18 Paper Acceptance Rate666of2,590submissions,26%Overall Acceptance Rate6,199of26,314submissions,24%

        Upcoming Conference

        CHI '24
        CHI Conference on Human Factors in Computing Systems
        May 11 - 16, 2024
        Honolulu , HI , USA

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader