To the Bookstore! Autonomous Wheelchair Navigation in an Urban Environment

  • Corey Montella
  • Timothy Perkins
  • John Spletzer
  • Michael Sands
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 92)


In this paper, we demonstrate reliable navigation of a smart wheelchair system (SWS) in an urban environment. Urban environments present unique challenges for service robots. They require localization accuracy at the sidewalk level, but compromise GPS position estimates through significant multi-path effects. However, they are also rich in landmarks that can be leveraged by feature-based localization approaches. To this end, our SWS employed a map-based localization approach. A map of the environment was acquired using a server vehicle, synthesized a priori, and made accessible to the SWS. The map embedded not only the locations of landmarks, but also semantic data delineating 7 different landmark classes to facilitate robust data association. Landmark segmentation and tracking by the SWS was then accomplished using both 2D and 3D LIDAR systems. The resulting localization method has demonstrated decimeter level positioning accuracy in a global coordinate frame. The localization package was integrated into a ROS framework with a sample based motion planner and control loop running at 5 Hz to enable autonomous navigation. For validation, the SWS repeatedly navigated autonomously between Lehigh University’s Packard Laboratory and the University bookstore, a distance of approximately 1.0 km roundtrip.


  1. 1.
    R. Bostelman, J. Albus, Sensor experiments to facilitate robot use in assistive environemnts, in Proceedings of the International Conference on Pervasive Technologies Related to Assistive Environments, Athens, Greece, 2008Google Scholar
  2. 2.
    J. Xu, G.G. Grindle, B. Salatin, D. Ding, R.A. Cooper, Manipulability evaluation of the personal mobility and manipulation appliance (PerMMA), in International symposium on Quality of Life Technology, Las Vegas, Nevada, 2010Google Scholar
  3. 3.
    Eitan Marder-Eppstein, “costmap_2d,” Cited 31 May 2012
  4. 4.
    A. Elfes, Using occupancy grids for mobile robot perception and navigation. IEEE Comput. Mag. 22, 56–57 (1989)CrossRefGoogle Scholar
  5. 5.
    M. Fischler, R. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  6. 6.
    C. Gao, I. Hoffman, T. Miller, T. Panzarella, J. Spletzer, Autonomous docking of a smart wheelchair for the automated transport and retrieval system (ATRS). J. Field Robot. 25, 203–222 (2008)CrossRefGoogle Scholar
  7. 7.
    C. Gao, M. Sands, J. Spletzer, Towards autonomous wheelchair systems in urban environments, in Proceedings of the International Conference on Field and Service Robotics (FSR), Cambridge, Massachusetts, 2009Google Scholar
  8. 8.
    A. Georgiev, P.K. Allen, Localization methods for a mobile robot in urban environments. IEEE Trans. Robot. 20–5, 851–864 (2004)CrossRefGoogle Scholar
  9. 9.
    B.P. Gerkey, K. Konolige, Planning and control in unstructured terrain, in ICRA Workshop on Path Planning and Costmaps, Pasadena, California, 2008Google Scholar
  10. 10.
    S. Hemachandra, T. Kollar, N. Roy, S. Teller, Following and interpreting narrated guided tours, in Proceedings of the International Conference on Robotics and Automation (ICRA), Shanghai, China, 2011Google Scholar
  11. 11.
    IFM Effector, “O3D200 3D image sensor product specification.” Cited 31 May 2012
  12. 12.
    Microsoft, “Xbox Support - Lighting,” Cited 31 May 2012
  13. 13.
    M. Montemerlo, S. Thru, D. Koller, B. Wegbreit, FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges, in Proceedings of the International Joint Conference on Artificial Intelligence, Acapulco, Mexico, 2003Google Scholar
  14. 14.
    I.R. Nourbakhsh, The Wheelchair Project, Cited 31 May 2012
  15. 15.
    M. Quigley, K. Conley, B.P. Gerkey, J. Faust, F. Tully, J. Leibs, R. Wheeler, A.Y. Ng, ROS: an open-source robot operating system, ICRA Workshop on Open Source Software, Kobe, Japan, 2009Google Scholar
  16. 16.
    F. Ramos, J. Nieto, H. Durrant-Whyte, Recognising and modelling landmarks to close loops in outdoor SLAM, in Proceedings of the International conference on Robotics and Automation (ICRA), Roma, Italy, 2007Google Scholar
  17. 17.
    R.B. Rusu, S. Cousin , 3D is here: point cloud library (PCL), in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 2011Google Scholar
  18. 18.
    C. Savtchenko J. Spletzer, in Sidewalk-leve people tracking with a low-cost 3D LIDAR system, Lehigh University Technical, Report LU-CSE-11-003, 2011Google Scholar
  19. 19.
    R. Simpson, Smart wheelchairs: a literature review. J. Rehabil. Res. Dev. 42, 423–436 (2005)CrossRefGoogle Scholar
  20. 20.
    S. Thrun, W. Burgard, D. Fox, Probabilistic Robotics, (The MIT Press, Cambrige, Massachusetts, 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Corey Montella
    • 1
  • Timothy Perkins
    • 1
  • John Spletzer
    • 1
  • Michael Sands
    • 1
  1. 1.Lehigh UniversityBethlehemUSA

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