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A vision-based indoor positioning systems utilizing computer aided design drawing

Authors:

Dae-ha Yoo,

Gaoyang Shan,

Byeong-hee RohAuthors Info & Claims

MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking

Pages 880 - 882

https://doi.org/10.1145/3495243.3558270

Published: 14 October 2022 Publication History

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Abstract

In recent years, with the increase in users' demand for location services, the research of Indoor Positioning Systems (IPS) has attracted much attention. Many researchers proposed schemes to estimate the user's location based on the Received Signal Strength Indicator (RSSI) values of wireless technologies. However, the RSSI value is affected by signal interference seriously. This causes the accuracy of the positioning to be greatly reduced. To solve this problem, we use Computer Vision (CV) to replace traditional solutions in this paper. CV is known for its high performance and low complexity. The proposed scheme is capable of inferring the current location of users in the possible candidates from the interior structural features of buildings captured by cameras and Computer-Aided Design (CAD) drawing.

References

[1]

Lidong Chen, Yin Zou, Yaohua Chang, Jinyun Liu, Benjamin Lin, and Zhigang Zhu. 2019. Multi-level scene modeling and matching for smartphone-based indoor localization. In 2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). IEEE, 311--316.

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[2]

Yunzhi Li, Rajeswari Hita Kambhamettu, Yidan Hu, and Rui Zhang. 2022. ImPos: An Image-Based Indoor Positioning System. In 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC). IEEE, 144--150.

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[3]

Han Xu, Zheng Yang, Zimu Zhou, Longfei Shangguan, Ke Yi, and Yunhao Liu. 2016. Indoor localization via multi-modal sensing on smartphones. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 208--219.

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Cited By

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Famili AHimona GKominis YStavrou AKovanis V(2024)Isochrons in Photonic Oscillators: A Paradigm Shift in Positioning2024 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking62109.2024.10619066(1-7)Online publication date: 3-Jun-2024
https://doi.org/10.23919/IFIPNetworking62109.2024.10619066
Cheng JRen PDeng T(2023)A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied EnvironmentsSensors10.3390/s2309436623:9(4366)Online publication date: 28-Apr-2023
https://doi.org/10.3390/s23094366
Zangenehnejad FGao Y(2023)Stochastic Modeling of Smartphones GNSS Observations Using LS-VCE and Application to Samsung S20Sensors10.3390/s2307347823:7(3478)Online publication date: 26-Mar-2023
https://doi.org/10.3390/s23073478
Show More Cited By

Index Terms

A vision-based indoor positioning systems utilizing computer aided design drawing
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression

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Information

Published In

MobiCom '22: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking

October 2022

932 pages

ISBN:9781450391818

DOI:10.1145/3495243

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: 14 October 2022

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Institute for Information and Communications Technology Promotion
Ministry of Science and ICT, South Korea

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ACM MobiCom '22

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SIGMOBILE

ACM MobiCom '22: The 28th Annual International Conference on Mobile Computing and Networking

October 17 - 21, 2022

NSW, Sydney, Australia

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Overall Acceptance Rate 440 of 2,972 submissions, 15%

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Cited By

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Famili AHimona GKominis YStavrou AKovanis V(2024)Isochrons in Photonic Oscillators: A Paradigm Shift in Positioning2024 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking62109.2024.10619066(1-7)Online publication date: 3-Jun-2024
https://doi.org/10.23919/IFIPNetworking62109.2024.10619066
Cheng JRen PDeng T(2023)A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied EnvironmentsSensors10.3390/s2309436623:9(4366)Online publication date: 28-Apr-2023
https://doi.org/10.3390/s23094366
Zangenehnejad FGao Y(2023)Stochastic Modeling of Smartphones GNSS Observations Using LS-VCE and Application to Samsung S20Sensors10.3390/s2307347823:7(3478)Online publication date: 26-Mar-2023
https://doi.org/10.3390/s23073478
Zhu YZhang JZhu YZhang BMa W(2023)RBCN-Net: A Data-Driven Inertial Navigation Algorithm for PedestriansApplied Sciences10.3390/app1305296913:5(2969)Online publication date: 25-Feb-2023
https://doi.org/10.3390/app13052969
Barshandeh SKoulaeizadeh SMasdari MAbdollahZadeh BGhasembaglou M(2023)A learning-based metaheuristic administered positioning model for 3D IoT networksApplied Soft Computing10.1016/j.asoc.2023.110113136:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110113

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