Unsupervised construction of an indoor floor plan using a smartphone

Hyojeong Shin, Yohan Chon, Hojung Cha

Research output: Contribution to journalArticlepeer-review

134 Citations (Scopus)


Indoor pedestrian tracking extends location-based services to indoor environments. Typical indoor positioning systems employ a training/positioning model using Wi-Fi fingerprints. While these approaches have practical results in terms of accuracy and coverage, they require an indoor map, which is typically not available to the average user and involves significant training costs. A practical indoor pedestrian tracking approach should consider the indoor environment without a pretrained database or floor plan. In this paper, we present an indoor pedestrian tracking system, called SmartSLAM, which automatically constructs an indoor floor plan and radio fingerprint map for anonymous buildings using a smartphone. The scheme employs odometry tracing using inertial sensors, an observation model using Wi-Fi signals, and a Bayesian estimation for floor-plan construction. SmartSLAM is a true simultaneous localization and mapping implementation that does not necessitate additional devices, such as laser rangefinders or wheel encoders. We implemented the scheme on off-the-shelf smartphones and evaluated the performance in our university buildings. Despite inherent tracking errors from noisy sensors, SmartSLAM successfully constructed indoor floor plans.

Original languageEnglish
Article number6060924
Pages (from-to)889-898
Number of pages10
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Issue number6
Publication statusPublished - 2012

Bibliographical note

Funding Information:
Manuscript received March 21, 2011; accepted September 8, 2011. Date of publication October 25, 2011; date of current version October 12, 2012. This work was supported by the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology, Korea, under Grant 2011-0000156 and Grant 2011-0015332. This paper was recommended by Associate Editor J. Wang.

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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