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|Title:||VisioMap : lightweight 3-D scene reconstruction toward natural indoor localization||Authors:||Li, Feng
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2019||Source:||Li, F., Hao, J., Wang, J., Luo, J., He, Y., Yu, D. & Cheng, X. (2019). VisioMap : lightweight 3-D scene reconstruction toward natural indoor localization. IEEE Internet of Things Journal, 6(5), 8870-8882. https://dx.doi.org/10.1109/JIOT.2019.2924244||Project:||MOE2016-T2-2-022||Journal:||IEEE Internet of Things Journal||Abstract:||Most existing proposals for indoor localization are 'unnatural,' as they rely on sensing abilities not available to human beings. While such a mismatch causes complications in human-computer interactions and thus potentially reduces the usability and friendliness of a localization service, it is partially entailed by the need for low-cost/effort sensing with resource-limited mobile devices. Fortunately, recent developments in smart glasses (e.g., Google Glasses) signal a trend toward realistic visual sensing and hence make the sensing ability of mobile devices more compatible to that of human users. Leveraging such front-end developments, we propose VisioMap as a natural indoor localization system that intentionally mimics the human skills in visual localization. VisioMap uses very sparse photograph samples to reconstruct 3-D indoor scenes; this is facilitated by the facts that photographs are taken at the eye-level with high stability and regularity, and that the reconstruction is lightweight as it exploits geometric features rather than image pixels. Localization is in turn performed by matching the geometric features extracted online to the reconstructed 3-D scene, making VisioMap: 1) natural to users as they can see the matched 3-D scene and 2) dispensed with the need for dense fingerprints/POIs toward accurate localization.||URI:||https://hdl.handle.net/10356/148581||ISSN:||2327-4662||DOI:||10.1109/JIOT.2019.2924244||Rights:||© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JIOT.2019.2924244.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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