research-article

IDyLL: indoor localization using inertial and light sensors on smartphones

Authors:

Steve HranilovicAuthors Info & Claims

UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 307 - 318

https://doi.org/10.1145/2750858.2807540

Published: 07 September 2015 Publication History

Abstract

Location-based services have experienced substantial growth in the last decade. However, despite extensive research efforts, sub-meter location accuracy with low-cost infrastructure continues to be elusive. In this paper, we propose IDyLL -- an indoor localization system using inertial measurement units (IMU) and photodiode sensors on smartphones. Using a novel illumination peak detection algorithm, IDyLL augments IMU-based pedestrian dead reckoning with location fixes. We devise a robust particle filter framework to mitigate identity ambiguity due to the lack of communication capability of conventional luminaries and sensing errors. Experimental study using data collected from smartphones shows that IDyLL is able to achieve high localization accuracy at low costs. Mean location errors of 0.38 m, 0.42 m, and 0.74 m are reported from multiple walks in three buildings with different luminary arrangements, respectively.

References

[1]

2014. Location-Based Services Reports. (2014). Accessed: 2014-7-29 from http://www.pewinternet.org/2013/09/12/location-based-services/.

[2]

Klaithem Al Nuaimi and Hesham Kamel. 2011. A survey of indoor positioning systems and algorithms. In Innovations in Information Technology (IIT), 2011 International Conference on. IEEE, 185--190.

[3]

Paramvir Bahl and Venkata N Padmanabhan. 2000. RADAR: An in-building RF-based user location and tracking system. In INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings on, Vol. 2. IEEE, 775--784.

[4]

Mehmet Bilgi, Murat Yuksel, and Nezih Pala. 2010. 3D optical wireless localization. In GLOBECOM Workshops (GC Wkshps). IEEE, 1062--1066.

[5]

Pavel Davidson, Jussi Collin, and Jarmo Takala. 2010. Application of particle filters for indoor positioning using floor plans. In Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), 2010. IEEE, 1--4.

[6]

Arnaud Doucet, Nando De Freitas, and Neil Gordon. 2001. An introduction to sequential Monte Carlo methods. In Sequential Monte Carlo methods in practice. Springer, 3--14.

[7]

Arnaud Doucet and Adam M Johansen. 2009. A tutorial on particle filtering and smoothing: Fifteen years later. Handbook of Nonlinear Filtering 12 (2009), 656--704.

[8]

Andrew R Golding and Neal Lesh. 1999. Indoor navigation using a diverse set of cheap, wearable sensors. In Wearable Computers, The Third International Symposium on. IEEE, 29--36.

Digital Library

[9]

Fredrik Gustafsson. 2010. Particle filter theory and practice with positioning applications. IEEE Aerospace and Electronic Systems Magazine 25, 7 (July 2010), 53--82.

[10]

Robert Harle. 2013. A survey of indoor inertial positioning systems for pedestrians. IEEE Communications Surveys and Tutorials 15, 3 (2013), 1281--1293.

[11]

Pan Hu, Liqun Li, Chunyi Peng, Guobin Shen, and Feng Zhao. 2013. Pharos: Enable physical analytics through visible light based indoor localization. In Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks. ACM, 5.

Digital Library

[12]

Antonio R Jimenez, Francisco Zampella, and Fernando Seco. 2013. Light-matching: a new signal of opportunity for pedestrian indoor navigation. In Indoor Positioning and Indoor Navigation (IPIN), 2013 International Conference on. IEEE, 1--10.

[13]

Soo-Yong Jung, Chang-Kuk Choi, Sang Hu Heo, Seong Ro Lee, and Chang-Soo Park. 2013. Received signal strength ratio based optical wireless indoor localization using light emitting diodes for illumination. In Consumer Electronics (ICCE), 2013 IEEE International Conference on. 63--64.

[14]

Incheol Kim, Eunmi Choi, and Huikyung Oh. 2012. Indoor User Tracking with Particle Filter. In COGNITIVE 2012, The Fourth International Conference on Advanced Cognitive Technologies and Applications. 59--62.

[15]

Nisarg Kothari, Balajee Kannan, Evan D. Glasgwow, and M. Bernardine Dias. 2012. Robust Indoor Localization on a Commercial Smart Phone. Procedia Computer Science 10, RoboSense (Jan. 2012), 1114--1120.

[16]

Fan Li, Chunshui Zhao, Guanzhong Ding, Jian Gong, Chenxing Liu, and Feng Zhao. 2012. A reliable and accurate indoor localization method using phone inertial sensors. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing - UbiComp '12. ACM Press, New York, USA, 421.

Digital Library

[17]

Liqun Li, Pan Hu, Chunyi Peng, Guobin Shen, and Feng Zhao. 2014. Epsilon: A visible light based positioning system. In 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI 14). USENIX Association, 331--343.

Digital Library

[18]

Ryan Libby. 2012. A Simple Method for Reliable Footstep Detection in Embedded Sensor Platforms. (2012).

[19]

Kaikai Liu, Xinxin Liu, and Xiaolin Li. 2013. Guoguo: Enabling fine-grained indoor localization via smartphone. In Proceeding of the 11th annual international conference on Mobile systems, applications, and services. ACM, 235--248.

Digital Library

[20]

Alex T. Mariakakis, Souvik Sen, Jeongkeun Lee, and Kyu-Han Kim. 2014. SAIL: Single Access Point-based Indoor Localization. In Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '14). ACM, New York, NY, USA, 315--328.

Digital Library

[21]

Chunyi Peng, Guobin Shen, Yongguang Zhang, Yanlin Li, and Kun Tan. 2007. Beepbeep: a high accuracy acoustic ranging system using cots mobile devices. In Proceedings of the 5th international conference on Embedded networked sensor systems. ACM, 1--14.

Digital Library

[22]

Gregary B Prince and Thomas DC Little. 2012. A two phase hybrid RSS/AoA algorithm for indoor device localization using visible light. In Global Communications Conference (GLOBECOM). IEEE, 3347--3352.

[23]

Anshul Rai, Krishna Kant Chintalapudi, Venkata N Padmanabhan, and Rijurekha Sen. 2012. Zee: zero-effort crowdsourcing for indoor localization. In Proceedings of the 18th annual international conference on Mobile computing and networking. ACM, 293--304.

Digital Library

[24]

Nishkam Ravi and Liviu Iftode. 2007. Fiatlux: Fingerprinting rooms using light intensity. In Pervasive Computing, Adjunct Proceedings of the Fifth International Conference on.

[25]

Nirupam Roy, He Wang, and Romit Roy Choudhury. 2014. I am a smartphone and I can tell my user's walking direction. Proceedings of the 12th annual international conference on Mobile systems, applications, and services - MobiSys '14 (2014), 329--342.

Digital Library

[26]

Chinnapat Sertthin, Emiko Tsuji, Masao Nakagawa, Shigeru Kuwano, and Kazuji Watanabe. 2009. A switching estimated receiver position scheme for visible light based indoor positioning system. In Wireless Pervasive Computing, 2009. ISWPC 2009. 4th International Symposium on. IEEE, 1--5.

Digital Library

[27]

Yangzhu Wang, Ning Li, Xi Chen, and Miao Liu. 2014. Design and Implementation of an AHRS Based on MEMS Sensors and Complementary Filtering. Advances in Mechanical Engineering 2014 (2014), 1--11.

[28]

Allen Jong-Woei Whang, Yi-Yung Chen, and Yuan-Ting Teng. 2009. Designing uniform illumination systems by surface-tailored lens and configurations of LED arrays. Journal of display technology 5, 3 (2009), 94--103.

[29]

Jie Xiong and Kyle Jamieson. 2013. ArrayTrack: A Fine-Grained Indoor Location System. In NSDI. 71--84.

Digital Library

[30]

Qiang Xu and Rong Zheng. 2015. Automated Detection of Burned-out Luminaries Using Indoor Positioning. In IPIN 2015 Sixth International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015). Banff, Canada.

[31]

Zheng Yang, Chenshu Wu, and Yunhao Liu. 2012. Locating in fingerprint space: wireless indoor localization with little human intervention. In Proceedings of the 18th annual international conference on Mobile computing and networking. ACM, 269--280.

Digital Library

[32]

Masaki Yoshino, Shinichiro Haruyama, and Masao Nakagawa. 2008. High-accuracy positioning system using visible LED lights and image sensor. In Radio and Wireless Symposium. IEEE, 439--442.

[33]

Paul A Zandbergen. 2009. Accuracy of iPhone locations: A comparison of assisted GPS, WiFi and cellular positioning. Transactions in GIS 13, s1 (2009), 5--25.

[34]

W. Zhang and M. Kavehrad. 2012. A 2D indoor localization system based on visible light LED. In Photonics Society Summer Topical Meeting Series. IEEE, 80--81.

[35]

Pengfei Zhou, Mo Li, and Guobin Shen. 2014. Use it free: Instantly knowing your phone attitude. In Proceedings of the 20th annual international conference on Mobile computing and networking. ACM, 605--616.

Digital Library

[36]

Soumaya Zirari, Philippe Canalda, and François Spies. 2010. WiFi GPS based combined positioning algorithm. In Wireless Communications, Networking and Information Security (WCNIS), 2010 IEEE International Conference on. IEEE, 684--688.

Cited By

Bhushan SAhluwalia ADeshwal APal A(2024)Single Anchor-Based Infrastructure-less Localization Performance using UWB Radios2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)10.1109/DCOSS-IoT61029.2024.00100(639-646)Online publication date: 29-Apr-2024
https://doi.org/10.1109/DCOSS-IoT61029.2024.00100
Girgensohn APatel MBiehl J(2024)Radio-frequency-based indoor-localization techniques for enhancing Internet-of-Things applicationsPersonal and Ubiquitous Computing10.1007/s00779-020-01446-828:1(385-401)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s00779-020-01446-8
Vieyra RMegowan-Romanowicz CO'Brien DVieyra CJohnson-Glenberg M(2023)Harnessing the Digital Science Education RevolutionTheoretical and Practical Teaching Strategies for K-12 Science Education in the Digital Age10.4018/978-1-6684-5585-2.ch008(131-152)Online publication date: 6-Jan-2023
https://doi.org/10.4018/978-1-6684-5585-2.ch008
Show More Cited By

Index Terms

IDyLL: indoor localization using inertial and light sensors on smartphones
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems

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Published In

cover image ACM Conferences

UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2015

1302 pages

ISBN:9781450335744

DOI:10.1145/2750858

General Chairs:
Kenji Mase
Nagoya University, JP
,
Marc Langheinrich
Università della Svizzera italiana (USI), CH
,
Daniel Gatica-Perez
IDIAP-EPFL, CH
,
Program Chairs:
Hans Gellersen
Lancaster University, UK
,
Tanzeem Choudhury
Cornell University
,
Koji Yatani
Univerisity of Tokyo, JP

Copyright © 2015 ACM.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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ISTC-PC: Intel Science and Technology Center for Pervasive Computing

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Association for Computing Machinery

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Publication History

Published: 07 September 2015

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Natural Sciences and Engineering Research Council of Canada

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UbiComp '15

Sponsor:

Yahoo! Japan
SIGMOBILE
FX Palo Alto Laboratory, Inc.
ACM
Rakuten Institute of Technology
Microsoft
Bell Labs
SIGCHI
Panasonic
Telefónica
ISTC-PC

UbiComp '15: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 7 - 11, 2015

Osaka, Japan

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UbiComp '15 Paper Acceptance Rate 101 of 394 submissions, 26%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 5 - 9, 2024

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Cited By

Bhushan SAhluwalia ADeshwal APal A(2024)Single Anchor-Based Infrastructure-less Localization Performance using UWB Radios2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)10.1109/DCOSS-IoT61029.2024.00100(639-646)Online publication date: 29-Apr-2024
https://doi.org/10.1109/DCOSS-IoT61029.2024.00100
Girgensohn APatel MBiehl J(2024)Radio-frequency-based indoor-localization techniques for enhancing Internet-of-Things applicationsPersonal and Ubiquitous Computing10.1007/s00779-020-01446-828:1(385-401)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s00779-020-01446-8
Vieyra RMegowan-Romanowicz CO'Brien DVieyra CJohnson-Glenberg M(2023)Harnessing the Digital Science Education RevolutionTheoretical and Practical Teaching Strategies for K-12 Science Education in the Digital Age10.4018/978-1-6684-5585-2.ch008(131-152)Online publication date: 6-Jan-2023
https://doi.org/10.4018/978-1-6684-5585-2.ch008
Cai CPu HYe LJiang HLuo J(2023)Active Acoustic Sensing for “Hearing” Temperature Under Acoustic InterferenceIEEE Transactions on Mobile Computing10.1109/TMC.2021.309679222:2(661-673)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TMC.2021.3096792
Wen SGe ZYuan DChen YWen FXu JGuan W(2023)Enhanced Pedestrian Navigation on Smartphones With VLP-Assisted PDR IntegrationIEEE Sensors Journal10.1109/JSEN.2023.328262723:14(15952-15962)Online publication date: 15-Jul-2023
https://doi.org/10.1109/JSEN.2023.3282627
Sugimoto MSuenaga MWatanabe HNakamura MHashizume H(2023)Smartphone Indoor Positioning using Inertial and Ambient Light Sensors2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)10.1109/IPIN57070.2023.10332528(1-6)Online publication date: 25-Sep-2023
https://doi.org/10.1109/IPIN57070.2023.10332528
Tiku SPasricha S(2023)An Overview of Indoor Localization TechniquesMachine Learning for Indoor Localization and Navigation10.1007/978-3-031-26712-3_1(3-25)Online publication date: 30-Jun-2023
https://doi.org/10.1007/978-3-031-26712-3_1
Salem ZWeiss A(2022)Improved Spatiotemporal Framework for Human Activity Recognition in Smart EnvironmentSensors10.3390/s2301013223:1(132)Online publication date: 23-Dec-2022
https://doi.org/10.3390/s23010132
Wu JFeng YChang C(2022)LiLo: ADL Localization with Conventional Luminaries and Ambient Light SensorElectronics10.3390/electronics1116250311:16(2503)Online publication date: 11-Aug-2022
https://doi.org/10.3390/electronics11162503
Liang QSun YWang LLiu M(2022)A Novel Inertial-Aided Visible Light Positioning System Using Modulated LEDs and Unmodulated Lights as LandmarksIEEE Transactions on Automation Science and Engineering10.1109/TASE.2021.310570019:4(3049-3067)Online publication date: Oct-2022
https://doi.org/10.1109/TASE.2021.3105700
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