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Comparison of Indoor Positioning Methods Based on AR Visual and WiFi Fingerprinting Method

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Web and Wireless Geographical Information Systems (W2GIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13238))

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Abstract

Location-based service (LBS) has become an indispensable part of our daily life. However, indoor positioning system at early stage is not able to meet the urgent need for indoor LBS. Low-cost indoor positioning technology without additional equipment is the current challenge in LBS field. In this paper, two typical indoor positioning methods are selected: AR (Augmented Reality) based visual positioning method and WiFi based positioning method. Experiments are conducted to compare the two indoor positioning methods from multiple perspectives. Results show that performance of the two methods are similar in the aspects such as positioning time consumption, equipment cost, usability and difficulty level during preprocessing. Main differences between them are as follows: AR visual positioning method is more accurate and stable, with its mean average error at around 0.85 m and max error at 3.18 m. It’s suitable for indoor environment rich in texture and stable in light. WiFi positioning has high values in error related variables. Its MAE is about 3 m and more volatile with extreme values. However, it has an edge in usability including power consumption indicator. It’s more efficient in data acquisition stage and is suitable for large-scale positioning. This paper tends to provide reference for selection of indoor positioning methods.

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Funding

This work is partially supported by the projects funded by the National Natural Science Foundation of China (Grant Number: 41771410) and the Ministry of Education of China (Grant Number: Ministry of Education of Humanities and Social Science Project 19JZD023).

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Authors and Affiliations

  1. Key Laboratory of Geographic Information Science (Ministry of Education) and School of Geographic Sciences, East China Normal University, Shanghai, 200241, China

    Yijun He & Xiang Li

  2. Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, East China Normal University, Shanghai, 200241, China

    Xiang Li

Authors
  1. Yijun He
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  2. Xiang Li
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Corresponding author

Correspondence to Xiang Li .

Editor information

Editors and Affiliations

  1. Institute of Science and Technology Austria, Klosterneuburg, Austria

    Farid Karimipour

  2. Universität Konstanz, Konstanz, Baden-Württemberg, Germany

    Sabine Storandt

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He, Y., Li, X. (2022). Comparison of Indoor Positioning Methods Based on AR Visual and WiFi Fingerprinting Method. In: Karimipour, F., Storandt, S. (eds) Web and Wireless Geographical Information Systems. W2GIS 2022. Lecture Notes in Computer Science, vol 13238. Springer, Cham. https://doi.org/10.1007/978-3-031-06245-2_13

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  • DOI: https://doi.org/10.1007/978-3-031-06245-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06244-5

  • Online ISBN: 978-3-031-06245-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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