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Accurate Smartphone Indoor Visual Positioning Based on a High-Precision 3D Photorealistic Map

Sensors (Basel, Switzerland), 2018-06, Vol.18 (6), p.1974 [Peer Reviewed Journal]

2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2018. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2018 by the authors. 2018 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s18061974 ;PMID: 29925779

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  • Title:
    Accurate Smartphone Indoor Visual Positioning Based on a High-Precision 3D Photorealistic Map
  • Author: Wu, Teng ; Liu, Jingbin ; Li, Zheng ; Liu, Keke ; Xu, Beini
  • Subjects: Accuracy ; Artificial intelligence ; Datasets ; Geometry ; image feature matching ; Indoor environments ; indoor visual positioning ; International conferences ; Localization ; Location based services ; Mapping ; Methods ; Outdoors ; Parameters ; Pattern recognition ; photogrammetric vision ; place recognition ; Remote sensing ; smartphone positioning ; Smartphones
  • Is Part Of: Sensors (Basel, Switzerland), 2018-06, Vol.18 (6), p.1974
  • Description: Indoor positioning is in high demand in a variety of applications, and indoor environment is a challenging scene for visual positioning. This paper proposes an accurate visual positioning method for smartphones. The proposed method includes three procedures. First, an indoor high-precision 3D photorealistic map is produced using a mobile mapping system, and the intrinsic and extrinsic parameters of the images are obtained from the mapping result. A point cloud is calculated using feature matching and multi-view forward intersection. Second, top-K similar images are queried using hamming embedding with SIFT feature description. Feature matching and pose voting are used to select correctly matched image, and the relationship between image points and 3D points is obtained. Finally, outlier points are removed using P3P with the coarse focal length. Perspective-four-point with unknown focal length and random sample consensus are used to calculate the intrinsic and extrinsic parameters of the query image and then to obtain the positioning of the smartphone. Compared with established baseline methods, the proposed method is more accurate and reliable. The experiment results show that 70 percent of the images achieve location error smaller than 0.9 m in a 10 m × 15.8 m room, and the prospect of improvement is discussed.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s18061974
    PMID: 29925779
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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