Indoor Floor Plan Construction Through Sensing Data Collected from Smartphones

Zhe Peng, Shang Gao, Bin Xiao, Guiyi Wei, Songtao Guo, Yuanyuan Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

With the development of sensing technology, smartphones can provide various kinds of data, including inertial sensing data, WiFi data, depth data, and images. These data make it possible to construct accurate indoor floor plans that are the critical foundations of flourishing indoor location-based services for smartphone. However, even with the popular crowdsourcing approach, the wide construction of indoor floor plans has not yet to be realized due to the intensive time consumption. In this paper, we utilize deep learning techniques to build PlanSketcher, a system that enables one user to construct fine-grained and facility-labeled indoor floor plans accurately. First, the proposed system extracts novel integrated features to recognize diverse landmarks. Second, traverse-independent hallway topologies are constructed based on the sensing data, depth data, and images through the proposed hallway construction algorithms. Finally, PlanSketcher constructs the room shape and labels recognized facilities in their corresponding positions to generate a complete indoor floor plan. Because PlanSketcher exploits different kinds of data collected from smartphones with new feature extraction method, it can obtain accurate indoor floor plan topology and facility labels. We implement PlanSketcher and conduct extensive experiments in three large indoor settings. The evaluation results show that the 90th percentile accuracy of positions and orientations of facilities are 1 m-2.5 m and 4°-6°, while 85%-95% facilities are recognized and labeled precisely.

Original languageEnglish
Article number8428421
Pages (from-to)4351-4364
Number of pages14
JournalIEEE Internet of Things Journal
Volume5
Issue number6
DOIs
Publication statusPublished - 1 Dec 2018

Keywords

  • Energy consumption
  • Facility label
  • Indoor floor plan construction
  • Sensing data
  • Smartphone

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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