Robust Indoor Localization on a Commercial Smart-Phone

Nisarg Kothari, Balajee Kannan, and M Bernardine Dias
tech. report CMU-RI-TR-11-27, Robotics Institute, Carnegie Mellon University, August, 2011


Download
  • Adobe portable document format (pdf) (1MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
This technical report outlines a system for robust indoor localization on a commercial smart-phone. Towards achieving effective localization, in the absence of GPS, we combine complementary localization algorithms of dead reckoning and Wifi signal strength fingerprinting. Dead reckoning is performed using the on-board accelerometer, magnetometer, and gyroscope sensors to detect motion and estimate orientation. At the same time, Wifi signal strength fingerprinting is employed to provide an independent position estimate. These measurements along with a pre-built map of the environment are combined using a particle filter towards more accurate pose estimation. We discuss a procedure for collecting Wifi calibration data which uses a robot to reduce the amount of time needed to train the system for a given environment. The system was tested using multiple participants in two different indoor environments. It was found to achieve localization accuracies of within 10 meters, sufficient for a variety of navigation and context-aware applications.

Keywords
GPS-free localization, RSSI fingerprinting, dead-reckoning solutions, indoor positioning system, Particle filters

Notes
Sponsor: Boeing Company
Associated Project(s): Human-Robot Teams

Text Reference
Nisarg Kothari, Balajee Kannan, and M Bernardine Dias, "Robust Indoor Localization on a Commercial Smart-Phone," tech. report CMU-RI-TR-11-27, Robotics Institute, Carnegie Mellon University, August, 2011

BibTeX Reference
@techreport{Kothari_2011_6902,
   author = "Nisarg Kothari and Balajee Kannan and M Bernardine Dias",
   title = "Robust Indoor Localization on a Commercial Smart-Phone",
   booktitle = "",
   institution = "Robotics Institute",
   month = "August",
   year = "2011",
   number= "CMU-RI-TR-11-27",
   address= "Pittsburgh, PA",
}