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Indoor Map Construction via Mobile Crowdsensing

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

Abstract

The lack of indoor maps is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this chapter, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size, and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, and then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes, and shapes. It also identifies different types of connection areas (e.g., escalators, stairs) between stories, and employs a refinement algorithm to correct detection errors. Our experiments on three stories of two large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1 \(\sim \) 2 m and 5 \(\sim \) 9\(^\circ \), while the hallway connectivity and connection areas between stories are 100% correct.

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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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