Jigsaw : Indoor Floor Plan Reconstruction via Mobile Crowdsensing

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Abstract

The lack of floor plans 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 paper, 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, 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. Our experiments on 3 stories of 2 large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1 ∼ 2m and 5 ∼ 9◦, while the hallway connectivity is 100% correct.

Author-supplied keywords

  • indoor floor plan reconstruction
  • mobile crowdsensing
  • model inference
  • probabilistic graphical

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