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iMoon: Using Smartphones for Image-based Indoor Navigation

Authors:

Marius Noreikis,

Antti Ylä-JääskiAuthors Info & Claims

SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

Pages 85 - 97

https://doi.org/10.1145/2809695.2809722

Published: 01 November 2015 Publication History

Abstract

The adoption of indoor navigation for smartphones has been relatively slow in the past years, although it would be direly needed in complex indoor areas. The primary barriers for its adoption include the lack of fine-grained and up-to-date indoor maps and the potential deployment and maintenance cost. In this paper we investigate the feasibility of utilizing crowdsourced data for building a smartphone-based indoor navigation system, focusing on the technical challenges caused by the varying quality of crowdsourced data. We developed iMoon, an indoor navigation system based on sensor-enriched 3D models of indoor environment, and evaluated its performance via a field study in a public building covering around 1,100 square meters.

iMoon builds 3D models of indoor environment from crowdsourced 2D photos, and compiles a navigation mesh from the generated 3D models. Depending on the input for 3D modelling, indoor pedestrian paths may be partly missing from the output. iMoon solves this issue by integrating into navigation mesh the pedestrian paths recognized from crowdsourced user motion trajectories. With photo-based 3D models, iMoon supports image-based localization that identifies user's position and facing direction with photos, and provides visual navigation instructions that show when and where to turn. To reduce response delay while maintaining the accuracy of localization, iMoon partitions 3D models based on the density of 3D points, and selects partitions for image-based localization using Wi-Fi fingerprints. According to our experimental results, iMoon works properly, and our solution of indoor localization achieves competitive performance compared with traditional approaches: in most cases, a user can be located within an average of 3.85 seconds with a location error of less than 2 meters and a facing direction error of less than 6 degrees.

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Cited By

Bai SWen WLi YShi CHsu L(2024)Toward Persistent Spatial Awareness: A Review of Pedestrian Dead Reckoning-Centric Indoor Positioning With SmartphonesIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.347004373(1-28)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3470043
Liu SHuang ZLi JLi AHuang X(2023)FILNet: Fast Image-Based Indoor Localization Using an Anchor Control NetworkSensors10.3390/s2319814023:19(8140)Online publication date: 28-Sep-2023
https://doi.org/10.3390/s23198140
Hu YFan XYin ZQian FJi ZShu YHan YXu QLiu JBahl PCosta XAl Hassanieh HAsadi ACox LPerino DWidmer JGiustiniano D(2023)The Wisdom of 1,170 Teams: Lessons and Experiences from a Large Indoor Localization CompetitionProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3592507(1-15)Online publication date: 2-Oct-2023
https://dl.acm.org/doi/10.1145/3570361.3592507
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Published In

cover image ACM Conferences

SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

November 2015

526 pages

ISBN:9781450336314

DOI:10.1145/2809695

General Chair:
Junehwa Song
KAIST, South Korea
,
Program Chairs:
Tarek Abdelzaher
University of Illinois at Urbana-Champaign, USA
,
Cecilia Mascolo
University of Cambridge, UK

Copyright © 2015 Owner/Author.

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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Published: 01 November 2015

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Academy of Finland

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SenSys '15

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SenSys '15: The 13th ACM Conference on Embedded Network Sensor Systems

November 1 - 4, 2015

Seoul, South Korea

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SenSys '15 Paper Acceptance Rate 27 of 132 submissions, 20%;

Overall Acceptance Rate 198 of 990 submissions, 20%

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Cited By

Bai SWen WLi YShi CHsu L(2024)Toward Persistent Spatial Awareness: A Review of Pedestrian Dead Reckoning-Centric Indoor Positioning With SmartphonesIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.347004373(1-28)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3470043
Liu SHuang ZLi JLi AHuang X(2023)FILNet: Fast Image-Based Indoor Localization Using an Anchor Control NetworkSensors10.3390/s2319814023:19(8140)Online publication date: 28-Sep-2023
https://doi.org/10.3390/s23198140
Hu YFan XYin ZQian FJi ZShu YHan YXu QLiu JBahl PCosta XAl Hassanieh HAsadi ACox LPerino DWidmer JGiustiniano D(2023)The Wisdom of 1,170 Teams: Lessons and Experiences from a Large Indoor Localization CompetitionProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3592507(1-15)Online publication date: 2-Oct-2023
https://dl.acm.org/doi/10.1145/3570361.3592507
Patra JPanda SNegi VChinara S(2023)Computer Vision Based 3D Model Floor Construction for Smart Parking SystemInternet of Things. Advances in Information and Communication Technology10.1007/978-3-031-45882-8_3(36-48)Online publication date: 26-Oct-2023
https://doi.org/10.1007/978-3-031-45882-8_3
Zhou GXu SZhang SWang YXiang C(2022)Multi-Floor Indoor Localization Based on Multi-Modal SensorsSensors10.3390/s2211416222:11(4162)Online publication date: 30-May-2022
https://doi.org/10.3390/s22114162
Yan SSu YSun AJi YXiao JChen X(2022)Low-cost and lightweight indoor positioning based on computer visionProceedings of the 2022 4th Asia Pacific Information Technology Conference10.1145/3512353.3512378(169-175)Online publication date: 14-Jan-2022
https://dl.acm.org/doi/10.1145/3512353.3512378
Hu YQian FYin ZLi ZJi ZHan YXu QJiang W(2022)ExperienceProceedings of the 28th Annual International Conference on Mobile Computing And Networking10.1145/3495243.3517021(82-93)Online publication date: 14-Oct-2022
https://dl.acm.org/doi/10.1145/3495243.3517021
Chen JLi SLiu DLu W(2022)Indoor camera pose estimation via style‐transfer 3D modelsComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1271437:3(335-353)Online publication date: 2-Feb-2022
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Li QWu FChen G(2022)An Efficient, Fair, and Robust Image Pricing Mechanism for Crowdsourced 3D ReconstructionIEEE Transactions on Services Computing10.1109/TSC.2019.295390615:1(498-512)Online publication date: 1-Jan-2022
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Balakrishnan Selvakumaran SHall D(2022)From crowd to cloud: simplified automatic reconstruction of digital building assets for facility managementJournal of Facilities Management10.1108/JFM-02-2021-001720:3(401-436)Online publication date: 18-Jan-2022
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