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demonstration

Demo: Acoustic Sensing Based Indoor Floor Plan Construction Using Smartphones

Published: 04 October 2017 Publication History

Abstract

This demo presents BatMapper, an acoustics sensing technology for fast, fine-grained and low cost floor plan construction. BatMapper operates by emitting sound signal and capturing its reflections by two microphones on smartphones. We develop robust probabilistic echo-object association and outlier removal algorithms to identify the correspondence between distances and objects, thus the geometry of corridors. We compensate minute hand sway movements to identify small surface recessions, thus detecting doors automatically. Additionally, we leverage structure cues in indoor environments for user trace calibration. The demo will enable any person to hold the smartphone and walk along a corridor to map the corridor shape and detect doors in real-time.

References

[1]
Moustafa Alzantot and Moustafa Youssef. 2012. Crowdinside: automatic construction of indoor floorplans Proceedings of the 20th International Conference on Advances in Geographic Information Systems. ACM, 99--108.
[2]
Jiang Dong, Yu Xiao, Marius Noreikis, Zhonghong Ou, and Antti Yla-Jaaski. 2015. imoon: Using smartphones for image-based indoor navigation Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. ACM, 85--97.
[3]
Ruipeng Gao, Mingmin Zhao, Tao Ye, Fan Ye, Yizhou Wang, Kaigui Bian, Tao Wang, and Xiaoming Li. 2014. Jigsaw: Indoor floor plan reconstruction via mobile crowdsensing Proceedings of the 20th annual international conference on Mobile computing and networking. ACM, 249--260.
[4]
Kourosh Khoshelham and Sander Oude Elberink. 2012. Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors, Vol. 12, 2 (2012), 1437--1454.
[5]
Hongbo Liu, Yu Gan, Jie Yang, Simon Sidhom, Yan Wang, Yingying Chen, and Fan Ye. {n. d.}. Push the limit of WiFi based localization for smartphones ACM Mobicom 2012.
[6]
Hartmut Surmann, Andreas Nüchter, and Joachim Hertzberg. 2003. An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments. Robotics and Autonomous Systems Vol. 45, 3 (2003), 181--198.
[7]
Juan D Tardós, José Neira, Paul M Newman, and John J Leonard. 2002. Robust mapping and localization in indoor environments using sonar data. The International Journal of Robotics Research, Vol. 21, 4 (2002), 311--330.
[8]
Bing Zhou, Mohammed Elbadry, Ruipeng Gao, and Fan Ye. 2017. BatMapper: Acoustic Sensing Based Indoor Floor Plan Construction Using Smartphones Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 42--55.

Cited By

View all
  • (2022)Robust Human Face Authentication Leveraging Acoustic Sensing on SmartphonesIEEE Transactions on Mobile Computing10.1109/TMC.2020.304865921:8(3009-3023)Online publication date: 1-Aug-2022
  • (2019)Multi-Modal Face Authentication using Deep Visual and Acoustic FeaturesICC 2019 - 2019 IEEE International Conference on Communications (ICC)10.1109/ICC.2019.8761776(1-6)Online publication date: May-2019
  • (2018)EchoPrintProceedings of the 24th Annual International Conference on Mobile Computing and Networking10.1145/3241539.3241575(321-336)Online publication date: 15-Oct-2018

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Published In

cover image ACM Conferences
MobiCom '17: Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking
October 2017
628 pages
ISBN:9781450349161
DOI:10.1145/3117811
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 October 2017

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Author Tags

  1. acoustic sensing
  2. indoor floor plans
  3. smartphones

Qualifiers

  • Demonstration

Funding Sources

  • BJTU funding
  • Google Faculty Research Award

Conference

MobiCom '17
Sponsor:

Acceptance Rates

MobiCom '17 Paper Acceptance Rate 35 of 186 submissions, 19%;
Overall Acceptance Rate 440 of 2,972 submissions, 15%

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Cited By

View all
  • (2022)Robust Human Face Authentication Leveraging Acoustic Sensing on SmartphonesIEEE Transactions on Mobile Computing10.1109/TMC.2020.304865921:8(3009-3023)Online publication date: 1-Aug-2022
  • (2019)Multi-Modal Face Authentication using Deep Visual and Acoustic FeaturesICC 2019 - 2019 IEEE International Conference on Communications (ICC)10.1109/ICC.2019.8761776(1-6)Online publication date: May-2019
  • (2018)EchoPrintProceedings of the 24th Annual International Conference on Mobile Computing and Networking10.1145/3241539.3241575(321-336)Online publication date: 15-Oct-2018

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