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Fusion of wifi and vision based on smart devices for indoor localization

Published: 02 December 2018 Publication History

Abstract

Indoor localization is an important problem with a wide range of applications such as indoor navigation, robot mapping, especially augmented reality(AR). One of most important tasks in AR technology is to estimate the target objects' position information in real environment. The existed AR systems mostly utilize specialized marker to locate, some AR systems track real 3D object in real environment but need to get the the position information of index points in environment in advance. The above methods are not efficiency and limit the application of AR system, so that solving indoor localization problem has significant meaning for the development of AR technology. The development of computer vision (CV) techniques and the ubiquity of intelligent devices with cameras provides the foundation for offering accurate localization services. However, pure CV-based solutions usually involve hundreds of photos and pre-calibration to construct an densely sampled 3D model, which is a labor-intensive overhead for practical deployment. And a large amount of computation cost is difficult to satisfy the requirement for efficiency in mobile device. In this paper, we present iStart, a lightweight, easy deployed, image-based indoor localization system, which can be run on smart phone and VR/AR devices like HTC Vive, Google Glasses and so on. With core techniques rooted in data hierarchy scheme of WiFi fingerprints and photos, iStart also acquires user localization with a single photo of surroundings with high accuracy and short delay. Extensive experiments in various environments show that 90 percentile location deviations are less than 1 m, and 60 percentile location deviations are less than 0.5 m.

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Cited By

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  • (2024)A Sequential-Multi-Decision Scheme for WiFi Localization Using Vision-Based RefinementIEEE Transactions on Mobile Computing10.1109/TMC.2023.325389323:3(2321-2336)Online publication date: Mar-2024
  • (2024)An Efficient Wi-Fi–Vision Map Construction and Self-Maintenance Method for Indoor LocalizationIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.333139273(1-15)Online publication date: 2024
  • (2020)A Comprehensive Survey of Indoor Localization Methods Based on Computer VisionSensors10.3390/s2009264120:9(2641)Online publication date: 6-May-2020

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cover image ACM Conferences
VRCAI '18: Proceedings of the 16th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry
December 2018
200 pages
ISBN:9781450360876
DOI:10.1145/3284398
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]

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Published: 02 December 2018

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Author Tags

  1. CV model
  2. image-based localization
  3. indoor localization
  4. smart devices
  5. wifi fingerprint

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View all
  • (2024)A Sequential-Multi-Decision Scheme for WiFi Localization Using Vision-Based RefinementIEEE Transactions on Mobile Computing10.1109/TMC.2023.325389323:3(2321-2336)Online publication date: Mar-2024
  • (2024)An Efficient Wi-Fi–Vision Map Construction and Self-Maintenance Method for Indoor LocalizationIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2023.333139273(1-15)Online publication date: 2024
  • (2020)A Comprehensive Survey of Indoor Localization Methods Based on Computer VisionSensors10.3390/s2009264120:9(2641)Online publication date: 6-May-2020

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