Motion-Aided Network SLAM

  • Joseph DjugashEmail author
  • Sanjiv Singh
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)


A key problem in the deployment of sensor networks is that of determining the location of each sensor such that subsequent data gathered can be registered. We would also like the network to provide localization for mobile entities, allowing them to navigate and explore the environment. In this paper, we present a thorough evaluation of our algorithm for localizing and mapping the mobile and stationary nodes in a sparsely connected sensor network using range-only measurements and odometry from the mobile node. Our approach utilizes an Extended Kalman Filter (EKF) in polar space allowing us to model the nonlinearity within the range-only measurements using Gaussian distributions. We demonstrate the effectiveness of our approach using experiments in realistic obstacle-filled environments that not only limit network connectivity but also introduce additional noise to the range data. Our results reveal that our proposed method offers good accuracy in these challenging environments even when little to no prior information is available.


Mobile Robot Mobile Node Extend Kalman Filter Belief State Polar Parameterization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Djugash, J., Singh, S., Kantor, G., Zhang, W.: Range-only slam for robots operating cooperatively with sensor networks. In: IEEE Int’l Conf. on Robotics and Automation, ICRA (2006)Google Scholar
  2. 2.
    Djugash, J., Singh, S., Grocholsky, B.P.: Decentralized mapping of robot-aided sensor networks. In: IEEE International Conference on Robotics and Automation (May 2008)Google Scholar
  3. 3.
    Bahl, P., Padmanabhan, V.: Radar: An in-building RF-based user location and tracking system. In: Proc. of the IEEE Infocom 2000, Tel Aviv, Israel (March 2000)Google Scholar
  4. 4.
    Priyantha, N., Chakraborty, A., Balakrishman, H.: The cricket location support system. In: Proc. of the 6th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MOBICOM), Boston, MA (August 2000)Google Scholar
  5. 5.
    Smith, A., Balakrishnan, H., Goraczko, M., Priyantha, N.B.: Tracking Moving Devices with the Cricket Location System. In: 2nd International Conference on Mobile Systems, Applications and Services (Mobisys), Boston, MA (June 2004)Google Scholar
  6. 6.
    nanoloc trx transceiver (na5tr1), Nanotron Technologies, Datasheet NA-06-0230-0388-2.00 (April 2008)Google Scholar
  7. 7.
    Borg, I., Groenen, P.: Modern multidimensional scaling: theory and applications. Springer, New York (1997)Google Scholar
  8. 8.
    Moore, D., Leonard, J., Rus, D., Teller, S.: Robust distributed network localization with noisy range measurements. In: SenSys 2004: Proc. 2nd International Conference on Embedded Networked Sensor Systems, pp. 50–61. ACM Press, New York (2004)Google Scholar
  9. 9.
    Olson, E., Leonard, J., Teller, S.: Robust range-only beacon localization. In: Proceedings of Autonomous Underwater Vehicles (2004)Google Scholar
  10. 10.
    Djugash, J., Singh, S.: A robust method of localization and mapping using only range. In: International Symposium on Experimental Robotics (July 2008)Google Scholar
  11. 11.
    Faloutsos, C., Lin, K.-I.: FastMap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In: Carey, M.J., Schneider, D.A. (eds.) Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, San Jose, California, 22–25, pp. 163–174 (1995)Google Scholar
  12. 12.
    Djugash, J., Singh, S., Grocholsky, B.: Modeling mobile robot motion with polar representations. In: International Conference on Intelligent Robots and Systems (October 2009)Google Scholar
  13. 13.
    Brumback, B., Srinath, M.: A chi-square test for fault-detection in Kalman filters. IEEE Transactions on Automatic Control (1987)Google Scholar
  14. 14.
    Montemerlo, M., Roy, N., Thrun, S.: Perspectives on standardization in mobile robot programming: The Carnegie Mellon navigation (CARMEN) toolkit. In: Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, pp. 2436–2441 (2003)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.The Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations