Sensor fusion for flexible human-portable building-scale mapping

M.F. Fallon, H. Johannsson, J. Brookshire, S. Teller, J.J. Leonard

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper describes a system enabling rapid multi-floor indoor map building using a body-worn sensor system fusing information from RGB-D cameras, LIDAR, inertial, and barometric sensors. Our work is motivated by rapid response missions by emergency personnel, in which the capability for one or more people to rapidly map a complex indoor environment is essential for public safety. Human-portable mapping raises a number of challenges not encountered in typical robotic mapping applications including complex 6-DOF motion and the traversal of challenging trajectories including stairs or elevators. Our system achieves robust performance in these situations by exploiting state-of-the-art techniques for robust pose graph optimization and loop closure detection. It achieves real-time performance in indoor environments of moderate scale. Experimental results are demonstrated for human-portable mapping of several floors of a university building, demonstrating the system's ability to handle motion up and down stairs and to organize initially disconnected sets of submaps in a complex environment.
Original languageEnglish
Title of host publicationIntelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)978-1-4673-1737-5
Publication statusPublished - 1 Oct 2012


  • SLAM (robots)
  • barometers
  • body sensor networks
  • emergency services
  • optical radar
  • safety
  • sensor fusion
  • RGB-D camera
  • barometric sensor
  • body-worn sensor system
  • complex 6-DOF motion
  • flexible human-portable building-scale mapping
  • human-portable mapping
  • inertial sensor
  • loop closure detection
  • multifloor indoor map building
  • pose graph optimization
  • public safety
  • robotic mapping
  • Educational institutions
  • Floors
  • Image resolution
  • Robot sensing systems

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