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Indoor Localization by Photo-Taking of the Environment

  • Ruipeng Gao
  • Fan Ye
  • Guojie Luo
  • Jason Cong
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

Mainstream indoor localization technologies rely on RF signatures that require extensive human efforts to measure and periodically recalibrate signatures. The progress to ubiquitous localization remains slow. In this chapter, we explore Sextant, an alternative approach that leverages environmental reference objects such as store logos. A user uses a smartphone to obtain relative position measurements to such static reference objects for the system to triangulate the user location. Sextant leverages image matching algorithms to automatically identify the chosen reference objects by photo-taking, and we propose two methods to systematically address image matching mistakes that cause large localization errors. We formulate the benchmark image selection problem, prove its NP-completeness, and propose a heuristic algorithm to solve it. We also propose a couple of geographical constraints to further infer unknown reference objects. To enable fast deployment, we propose a lightweight site survey method for service providers to quickly estimate the coordinates of reference objects. Extensive experiments have shown that Sextant prototype achieves 2–5 m accuracy at 80-percentile, comparable to the industry state of the art, while covering a \(150\times 75\) m mall and \(300\times 200\) m train station requires a one-time investment of only 2–3 man-hours from service providers.

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Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Beijing Jiaotong UniversityBeijingChina
  2. 2.Stony Brook UniversityStony BrookUSA
  3. 3.Peking UniversityBeijingChina
  4. 4.UCLALos AngelesUSA

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