Localization, Route Planning, and Smartphone Interface for Indoor Navigation

  • Balajee Kannan
  • Nisarg Kothari
  • Chet Gnegy
  • Hend Gedaway
  • M. Freddie Dias
  • M. Bernardine Dias
Part of the Studies in Computational Intelligence book series (SCI, volume 507)


Low-cost navigation solutions for indoor environments have a variety of real-world applications ranging from emergency evacuation to mobility aids for people with disabilities. Primary challenges for commercial indoor navigation solutions include robust localization in the absence of GPS, efficient route-planning and re-planning techniques, and effective user interfaces for resource-constrained platforms like smartphones and mobile phones. In this chapter, we present an architecture for indoor navigation using an Android smartphone that integrates three core components of localization, map-representation, and user interface towards a robust and effective solution for guiding a variety of users, from sighted to the visually impaired to their intended destination. Specifically, we developed a navigation solution that combines complementary localization algorithms [10] of dead reckoning (DR) and WiFi signal strength fingerprinting (SSI) with enhanced route-planning algorithms to account for the sensory and mobility constraints of the user to efficiently plan safe routes and communicate the route information with sufficient resolution to address the needs of the users. To evaluate the feasibility of our solution, we develop a prototype application on a commercial smartphone and tested it in multiple indoor environments. The results show that the system was able to accurately estimate user location to within 5 m and subsequently provide effective navigation guidance to the user.


SSI-based localization Path-planning Assistive technology User interface 



This work is sponsored in part by the Google Core AI gift from Google Inc., The Boeing Company, Google Professor Partnership Award, CMU’s Traffic 21 Grant, Berkman Award, and CMU-Qatar Faculty seed funding. The content of this work does not necessarily reflect the position or policy of the sponsors and no official endorsement should be inferred. The authors would like to thank Sarah Belousov, Ermine Teves, Anna Kasunic and other members of the rCommerce Laboratory at Carnegie Mellon University for their valuable contributions during the needs assessment phase. We would also like to thank our community partners, Western Pennsylvania School for Blind Children, Western Pennsylvania School for the Deaf, and Blind and Vision Rehabilitation Services of Pittsburgh for their valuable feedback during design, development, and testing phases.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Balajee Kannan
    • 1
  • Nisarg Kothari
    • 2
  • Chet Gnegy
    • 3
  • Hend Gedaway
    • 4
  • M. Freddie Dias
    • 4
  • M. Bernardine Dias
    • 4
  1. 1.GE Global ResearchNiskayunaUSA
  2. 2.Google IncMountain ViewUSA
  3. 3.University of PittsburghPittsburghUSA
  4. 4.Carnegie Mellon UniversityPittsburghUSA

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