research-article

Smartphone-based Acoustic Indoor Space Mapping

Authors:

Swadhin Pradhan,

Bo YangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 2

Article No.: 75, Pages 1 - 26

https://doi.org/10.1145/3214278

Published: 05 July 2018 Publication History

Abstract

Constructing a map of indoor space has many important applications, such as indoor navigation, VR/AR, construction, safety, facility management, and network condition prediction. Existing indoor space mapping requires special hardware (e.g., indoor LiDAR equipment) and well-trained operators. In this paper, we develop a smartphone-based indoor space mapping system that lets a regular user quickly map an indoor space by simply walking around while holding a phone in his/her hand. Our system accurately measures the distance to nearby reflectors, estimates the user's trajectory, and pairs different reflectors the user encounters during the walk to automatically construct the contour. Using extensive evaluation, we show our contour construction is accurate: the median errors are 1.5 cm for a single wall and 6 cm for multiple walls (due to longer trajectory and the higher number of walls). We show that our system provides a median error of 30 cm and a 90-percentile error of 1 m, which is significantly better than the state-of-the-art smartphone acoustic mapping system BatMapper [64], whose corresponding errors are 60 cm and 2.5 m respectively, even after multiple walks. We further show that the constructed indoor contour can be used to predict wireless received signal strength (RSS).

References

[1]

2017. Affordable Scanning LiDAR for everyone. http://scanse.io. {Online} Last Accessed: 11/30/2017.

[2]

2017. AirLoc: Pedestrian dead reckoning for passenger localization. http://www.es.ewi.tudelft.nl/msc-theses/2017-deMoes.pdf. {Online} Last Accessed: 11/30/2017.

[3]

2017. Android ARCore. https://developers.google.com/ar/. {Online} Last Accessed: 11/30/2017.

[4]

2017. Apple ARKit. https://developer.apple.com/arkit/. {Online} Last Accessed: 11/30/2017.

[5]

2017. AR MeasureKit. https://itunes.apple.com/app/id1258270451. {Online} Last Accessed: 11/30/2017.

[6]

2017. Background noise reduction: one of your smartphone's greatest tools. http://www.techradar.com/news/phone-and-communications/mobile-phones/background-noise-reduction-one-of-your-smartphone-s-greatest-tools-1229667. {Online} Last Accessed: 11/30/2017.

[7]

2017. Bumblebee Camera. https://www.ptgrey.com/bumblebee2-firewire-stereo-vision-camera-systems. {Online} Last Accessed: 30/11/2017.

[8]

2017. Cook's Distance. http://bit.ly/2n2iQtv. {Online} Last Accessed: 11/30/2017.

[9]

2017. Housecraft App. https://itunes.apple.com/us/app/housecraft/id1261483849. {Online} Last Accessed: 11/30/2017.

[10]

2017. IKEA Place App. https://itunes.apple.com/us/app/ikea-place/id1279244498. {Online} Last Accessed: 11/30/2017.

[11]

2017. IPhone X. www.apple.com/iphone-x/. {Online} Last Accessed: 11/30/2017.

[12]

2017. Leica DISTO E7100i 200ft Laser Distance Measure with Bluetooth. http://amzn.to/2m2WSr4. {Online} Last Accessed: 11/30/2017.

[13]

2017. Lenovo Phab Series Smartphones. https://www3.lenovo.com/us/en/smart-devices/-lenovo-smartphones/phab-series/c/phab-series. {Online} Last Accessed: 11/30/2017.

[14]

2017. Microsoft Hololens. https://www.microsoft.com/microsoft-hololens/en-us. {Online} Last Accessed: 11/30/2017.

[15]

2017. Oculus VR Rift headset. https://www.oculus.com/rift/. {Online} Last Accessed: 11/30/2017.

[16]

2017. PMD{Vision} Camcube Depth Camera. https://www.youtube.com/watch?v=f0bYnxy74fE. {Online} Last Accessed: 30/11/2017.

[17]

2017. Portrait mode on the Pixel 2 and Pixel 2 XL smartphones. https://research.googleblog.com/2017/10/portrait-mode-on-pixel-2-and-pixel-2-xl.html. {Online} Last Accessed: 30/11/2017.

[18]

2017. Project Tango. https://get.google.com/tango/. {Online} Last Accessed: 11/30/2017.

[19]

2017. Sample compass code. https://github.com/iutinvg/compass. {Online} Last Accessed: 11/30/2017.

[20]

2017. THe average walking stride length. http://www.livestrong.com/article/206902-how-to-reset-a-new-balance-sport-pedometer/. {Online} Last Accessed: 11/30/2017.

[21]

Moustafa Alzantot and Moustafa Youssef. 2012. CrowdInside: Automatic Construction of Indoor Floorplans. In SIGSPATIAL '12. ACM, New York, NY, USA, 99--108.

Digital Library

[22]

Moustafa Alzantot and Moustafa Youssef. 2012. CrowdInside: Automatic Construction of Indoor Floorplans. In SIGSPATIAL '12. ACM, New York, NY, USA, 99--108.

Digital Library

[23]

S. Ayub, B. M. Heravi, A. Bahraminasab, and B. Honary. 2012. Pedestrian Direction of Movement Determination Using Smartphone. In 2012 Sixth International Conference on Next Generation Mobile Applications, Services and Technologies. 64--69.

Digital Library

[24]

Martin Azizyan, Ionut Constandache, and Romit Roy Choudhury. 2009. SurroundSense: Mobile Phone Localization via Ambience Fingerprinting. In MobiCom '09. 261--272.

Digital Library

[25]

Richard H. Bartels, John C. Beatty, and Brian A. Barsky. 1987. An Introduction to Splines for Use in Computer Graphics 8 Geometric Modeling. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

Digital Library

[26]

Si Chen, Muyuan Li, Kui Ren, and Chunming Qiao. 2015. Crowd Map: Accurate Reconstruction of Indoor Floor Plans from Crowdsourced Sensor-Rich Videos. In ICDCS 2015, Columbus, OH, USA. 1--10.

[27]

S. Chen, M. Li, K. Ren, and C. Qiao. 2015. Crowd Map: Accurate Reconstruction of Indoor Floor Plans from Crowdsourced Sensor-Rich Videos. In 2015 IEEE 35th International Conference on Distributed Computing Systems. 1--10.

[28]

R. Dennis Cook. 1979. Influential observations in linear regression. J. Amer. Statist. Assoc. 74, 365 (1979), 169--174.

[29]

Graham Daniel, Simmons George, T.Nguyen David, and Zhou Gang. 2015. A Software Based Sonar Ranging Sensor for Smart Phones. IoT (2015).

[30]

Ivan Dokmanića, Reza Parhizkara, Andreas Walthera, Yue M. Lub, and Martin Vetterlia. 2013. Acoustic echoes reveal room shape. Proceedings of National Academy of Sciences (PNAS) (2013).

[31]

Jiang Dong, Yu Xiao, Marius Noreikis, Zhonghong Ou, and Antti Ylä-Jääski. 2015. iMoon: Using Smartphones for Image-based Indoor Navigation. In SenSys. ACM, 85--97.

Digital Library

[32]

Jorge Fuentes-Pacheco, José Ruiz-Ascencio, and Juan Manuel Rendón-Mancha. 2015. Visual Simultaneous Localization and Mapping: A Survey. Artif. Intell. Rev. 43, 1 (2015), 55--81.

Digital Library

[33]

J. H. Gao and Li-Shiuan Peh. 2016. A smartphone-based laser distance sensor for outdoor environments. In 2016 IEEE International Conference on Robotics and Automation (ICRA). 2922--2929.

[34]

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. In MobiCom '14. ACM, 249--260.

Digital Library

[35]

Ravi Garg, Vijay Kumar B. G, and Ian D. Reid. 2016. Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue. CoRR abs/1603.04992 (2016). arXiv:1603.04992 http://arxiv.org/abs/1603.04992

[36]

Daniel Graham, Gang Zhou, Ed Novak, and Jeffrey Buffkin. 2016. A Smartphone Compatible SONAR Ranging Attachment for 2-D Mapping. IoT (2016).

[37]

Daniel Halperin, Wenjun Hu, Anmol Sheth, and David Wetherall. 2011. Tool Release: Gathering 802.11n Traces with Channel State Information. ACM SIGCOMM CCR 41, 1 (Jan. 2011), 53.

Digital Library

[38]

Yifei Jiang, Yun Xiang, Xin Pan, Kun Li, Qin Lv, Robert P. Dick, Li Shang, and Michael Hannigan. 2013. Hallway Based Automatic Indoor Floorplan Construction Using Room Fingerprints. In UbiComp '13. ACM, 315--324.

Digital Library

[39]

Yifei Jiang, Yun Xiang, Xin Pan, Kun Li, Qin Lv, Robert P. Dick, Li Shang, and Michael Hannigan. 2013. Hallway Based Automatic Indoor Floorplan Construction Using Room Fingerprints. In UbiComp '13. ACM, New York, NY, USA, 315--324.

Digital Library

[40]

Kourosh Khoshelham and Er Oude Elberink. 2012. Accuracy and resolution of kinect depth data for indoor mapping applications. In Sensors, 12, 1437--1454. 2013. 8238.

[41]

Kourosh Khoshelham and Er Oude Elberink. 2013. Accuracy and resolution of kinect depth data for indoor mapping applications. In Sensors 2012, 12, 1437--1454. 8238.

[42]

Miranda Krekovic, Ivan Dokmanic, and Martin Vetterli. 2016. EchoSLAM: Simultaneous localization and mapping with acoustic echoes. In ICASSP 2016, Shanghai, China, March 20-25, 2016. IEEE, 11--15.

[43]

Lawrence Berkeley National Laboratory, United States. Department of Energy. Office of Scientific, and Technical Information. 2001. The National Human Activity Pattern Survey (NHAPS): A Resource for Assessing Exposure to Environmental Pollutants. Lawrence Berkeley National Laboratory. https://books.google.com/books?id=njh3AQAACAAJ

[44]

Kaikai Liu, Xinxin Liu, and Xiaolin Li. 2013. Guoguo: Enabling Fine-grained Indoor Localization via Smartphone. In MobiSys '13. 235--248.

Digital Library

[45]

Wenguang Mao, Jian He, and Lili Qiu. 2016. High-precision Acoustic Motion Tracking. In MobiCom '16. 491--492.

Digital Library

[46]

Yuya Murata, Katsuhiko Kaji, Kei Hiroi, and Nobuo Kawaguchi. 2014. Pedestrian Dead Reckoning Based on Human Activity Sensing Knowledge. In UbiComp '14 Adjunct. ACM, New York, NY, USA, 797--806.

Digital Library

[47]

Rajalakshmi Nandakumar, Shyamnath Gollakota, and Nathaniel Watson. 2015. Contactless Sleep Apnea Detection on Smartphones. In MobiSys '15. ACM, New York, NY, USA, 45--57.

Digital Library

[48]

Rajalakshmi Nandakumar, Vikram Iyer, Desney Tan, and Shyamnath Gollakota. 2016. FingerIO: Using Active Sonar for Fine-Grained Finger Tracking. In CHI '16. 1515--1525.

Digital Library

[49]

Yasuharu Onishi, Jun Kuroda, Yukio Murata, Motoyoshi Komoda, Kazuyuki Tsunoda, Yukio Yokoyama, Yasuhiro Oikawa, and Yoshio Yamasaki. 2010. The acoustical design of mobile phones. Vol. 2. 1250--1257.

[50]

Chunyi Peng, Guobin Shen, Yongguang Zhang, Yanlin Li, and Kun Tan. 2007. BeepBeep: A High Accuracy Acoustic Ranging System Using COTS Mobile Devices. In SenSys '07. ACM, New York, NY, USA, 1--14.

Digital Library

[51]

Nirupam Roy, He Wang, and Romit Roy Choudhury. 2014. I am a smartphone and I can tell my users walking direction. In In Proceedings of ACM International Conference onMobile Systems, Applications, and Services (MobiSys).

Digital Library

[52]

R. Scheibler, I. Dokmanić, and M. Vetterli. 2015. Raking echoes in the time domain. In ICASSP '15. 554--558.

[53]

Wen-Yuah Shih, Liang-Yu Chen, and Kun-Chan Lan. 2012. Estimating Walking Distance with a Smart Phone. In PAAP '12. IEEE Computer Society, Washington, DC, USA, 166--171.

Digital Library

[54]

Yuanchao Shu, Kang G. Shin, Tian He, and Jiming Chen. 2015. Last-Mile Navigation Using Smartphones. In MobiCom '15. ACM, New York, NY, USA, 512--524.

Digital Library

[55]

Keith N. Snavely. 2009. Scene Reconstruction and Visualization from Internet Photo Collections. Ph.D. Dissertation. Seattle, WA, USA. Advisor(s) Seitz, Steve and Szeliski, Rick.

Digital Library

[56]

Arno Solin, Santiago Cortes, Esa Rahtu, and Juho Kannala. 2017. Inertial Odometry on Handheld Smartphones. CoRR abs/1703.00154 (2017).

[57]

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 3--4 (2003), 181--198.

[58]

Juan D. Tardós, José Neira, Paul M. Newman, and John J. Leonard. 2002. Robust Mapping and Localization in Indoor Environments Using Sonar Data. I. J. Robotic Res. 21, 4 (2002), 311--330.

[59]

He Wang, Souvik Sen, Ahmed Elgohary, Moustafa Farid, Moustafa Youssef, and Romit Roy Choudhury. 2012. No Need to War-drive: Unsupervised Indoor Localization. In MobiSys '12. ACM, New York, NY, USA, 197--210.

Digital Library

[60]

Tianben Wang, Daqing Zhang, Yuanqing Zheng, Tao Gu, Xingshe Zhou, and Bernadette Dorizzi. 2017. C-FMCW Based Contactless Respiration Detection Using Acoustic Signal. IMWUT 1, 4 (2017), 170:1--170:20.

Digital Library

[61]

Wei Wang, Alex X. Liu, and Ke Sun. 2016. Device-free Gesture Tracking Using Acoustic Signals. In MobiCom '16. ACM, New York, NY, USA, 82--94.

Digital Library

[62]

Sangki Yun, Yi-Chao Chen, and Lili Qiu. 2015. Turning a Mobile Device into a Mouse in the Air. In MobiSys '15. 15--29.

Digital Library

[63]

Huanle Zhang, Wan Du, Pengfei Zhou, Mo Li, and Prasant Mohapatra. 2016. DopEnc: Acoustic-based Encounter Profiling Using Smartphones. In MobiCom '16. 294--307.

Digital Library

[64]

Bing Zhou, Mohammed Elbadry, Ruipeng Gao, and Fan Ye. 2017. BatMapper: Acoustic Sensing Based Indoor Floor Plan Construction Using Smartphones. In MobiSys '17. ACM, New York, NY, USA, 42--55.

Digital Library

[65]

Pengfei Zhou, Mo Li, and Guobin Shen. 2014. Use It Free: Instantly Knowing Your Phone Attitude. In MobiCom '14. ACM, 605--616.

Digital Library

Cited By

Yu RLee SXie JBillah SCarroll J(2024)Human–AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM EraFuture Internet10.3390/fi1607025416:7(254)Online publication date: 18-Jul-2024
https://doi.org/10.3390/fi16070254
Luo WSong QYan ZTan RLin G(2024)Indoor Smartphone SLAM With Acoustic EchoesIEEE Transactions on Mobile Computing10.1109/TMC.2023.332339323:6(6634-6649)Online publication date: Jun-2024
https://doi.org/10.1109/TMC.2023.3323393
Ren YLi SChen CLiu HYu JChen YYang HLi H(2024)Robust Indoor Location Identification for Smartphones Using Echoes From Dominant ReflectorsIEEE Transactions on Mobile Computing10.1109/TMC.2023.3307695(1-17)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3307695
Show More Cited By

Index Terms

Smartphone-based Acoustic Indoor Space Mapping
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Ambient intelligence
      2. Mobile computing

Recommendations

RSSI Based Indoor Localization for Smartphone Using Fixed and Mobile Wireless Node
COMPSAC '13: Proceedings of the 2013 IEEE 37th Annual Computer Software and Applications Conference

Nowadays with the dispersion of wireless networks, smartphones and diverse related services, different localization techniques have been developed. Global Positioning System (GPS) has a high rate of accuracy for outdoor localization but the signal is ...
Improving indoor positioning precision by using received signal strength fingerprint and footprint based on weighted ambient Wi-Fi signals

Positioning is the foremost process in the location based service (LBS). With the GPS signal strength obstructed by the wall, indoor users cannot obtain their positions with the assistance from the global positioning satellites. Most of the indoor ...
Smartphone based intelligent indoor positioning using fuzzy logic
Abstract
Smartphones are indispensable helpers of people in their daily routine, including functions that serve the disabled or aged people finding their outdoor location. This paper presents an experimental investigation of the indoor ...
Highlights
- An experimental investigation of indoor positioning algorithms.
- A fuzzy logic ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 2

June 2018

741 pages

EISSN:2474-9567

DOI:10.1145/3236498

Issue’s Table of Contents

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: 05 July 2018

Accepted: 01 April 2018

Revised: 01 February 2018

Received: 01 November 2017

Published in IMWUT Volume 2, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

70
Total Citations
View Citations
961
Total Downloads

Downloads (Last 12 months)101
Downloads (Last 6 weeks)6

Reflects downloads up to 11 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yu RLee SXie JBillah SCarroll J(2024)Human–AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM EraFuture Internet10.3390/fi1607025416:7(254)Online publication date: 18-Jul-2024
https://doi.org/10.3390/fi16070254
Luo WSong QYan ZTan RLin G(2024)Indoor Smartphone SLAM With Acoustic EchoesIEEE Transactions on Mobile Computing10.1109/TMC.2023.332339323:6(6634-6649)Online publication date: Jun-2024
https://doi.org/10.1109/TMC.2023.3323393
Ren YLi SChen CLiu HYu JChen YYang HLi H(2024)Robust Indoor Location Identification for Smartphones Using Echoes From Dominant ReflectorsIEEE Transactions on Mobile Computing10.1109/TMC.2023.3307695(1-17)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3307695
Liu MYang SRathee ADu W(2024)Orientation Estimation Piloted by Deep Reinforcement Learning2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI)10.1109/IoTDI61053.2024.00016(134-145)Online publication date: 13-May-2024
https://doi.org/10.1109/IoTDI61053.2024.00016
Jiang XZhao ZLi ZHong F(2023)Echo-ID: Smartphone Placement Region Identification for Context-Aware ComputingSensors10.3390/s2309430223:9(4302)Online publication date: 26-Apr-2023
https://doi.org/10.3390/s23094302
Meng CJiang SWu MXiao XTao DGao R(2023)Smartphone-Based Indoor Floor Plan Construction via Acoustic Ranging and Inertial TrackingMachines10.3390/machines1102020511:2(205)Online publication date: 1-Feb-2023
https://doi.org/10.3390/machines11020205
Chhaglani BZakaria CGummeson JShenoy PHossein Motlagh NZaidan MRebeiro-Hargrave AArvind D(2023)BreathEasy: Exploring the Potential of Acoustic Sensing for Healthy Indoor EnvironmentsProceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities10.1145/3597064.3597338(25-30)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3597064.3597338
Radeta MRodrigues CSilva FAbreu PPestana JNguyen NZuniga AFlores HNurmi P(2023)Lost in the Deep?Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962457:2(1-27)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3596245
Cheng HLou WYang YChen YZhang X(2023)TwinkleTwinkleProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962387:2(1-30)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3596238
Chhaglani BAcer UJang SKawsar FMin C(2023)CocoonProceedings of the 24th International Workshop on Mobile Computing Systems and Applications10.1145/3572864.3580340(89-95)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1145/3572864.3580340
Show More Cited By

View Options

Get Access

Login options

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

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents