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Multi-sensor Mobile Robot Localization for Diverse Environments

  • Joydeep Biswas
  • Manuela Veloso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8371)

Abstract

Mobile robot localization with different sensors and algorithms is a widely studied problem, and there have been many approaches proposed, with considerable degrees of success. However, every sensor and algorithm has limitations, due to which we believe no single localization algorithm can be “perfect,” or universally applicable to all situations.

Laser rangefinders are commonly used for localization, and state-of-the-art algorithms are capable of achieving sub-centimeter accuracy in environments with features observable by laser rangefinders. Unfortunately, in large scale environments, there are bound to be areas devoid of features visible by a laser rangefinder, like open atria or corridors with glass walls. In such situations, the error in localization estimates using laser rangefinders could grow in an unbounded manner. Localization algorithms that use depth cameras, like the Microsoft Kinect sensor, have similar characteristics. WiFi signal strength based algorithms, on the other hand, are applicable anywhere there is dense WiFi coverage, and have bounded errors. Although the minimum error of WiFi based localization may be greater than that of laser rangefinder or depth camera based localization, the maximum error of WiFi based localization is bounded and less than that of the other algorithms.

Hence, in our work, we analyze the strengths of localization using all three sensors - using a laser rangefinder, a depth camera, and using WiFi. We identify sensors that are most accurate at localization for different locations on the map. The mobile robot could then, for example, rely on WiFi localization more in open areas or areas with glass walls, and laser rangefinder and depth camera based localization in corridor and office environments.

Keywords

Localization Mobile Robots Sensor Fusion 

References

  1. 1.
    Thrun, S., Bennewitz, M., Burgard, W., Cremers, A., Dellaert, F., Fox, D., Hahnel, D., Rosenberg, C., Roy, N., Schulte, J., et al.: Minerva: A second-generation museum tour-guide robot. In: 1999 IEEE International Conference on Robotics and Automation, vol. 3 (1999)Google Scholar
  2. 2.
    Romero, L., Morales, E., Sucar, E.: An exploration and navigation approach for indoor mobile robots considering sensor’s perceptual limitations. In: 2001 IEEE International Conference on Robotics and Automation, vol. 3, pp. 3092–3097. IEEE (2001)Google Scholar
  3. 3.
    Nilsson, N.: Shakey the robot. Technical report, DTIC Document (1984)Google Scholar
  4. 4.
    Buhmann, J., Burgard, W., Cremers, A., Fox, D., Hofmann, T., Schneider, F., Strikos, J., Thrun, S.: The mobile robot rhino. AI Magazine 16(2), 31 (1995)Google Scholar
  5. 5.
    Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)CrossRefGoogle Scholar
  6. 6.
    Koenig, S., Simmons, R.: Xavier: A robot navigation architecture based on partially observable markov decision process models. In: Artificial Intelligence Based Mobile Robotics: Case Studies of Successful Robot Systems, pp. 91–122 (1998)Google Scholar
  7. 7.
    Nourbakhsh, I., Kunz, C., Willeke, T.: The mobot museum robot installations: A five year experiment. In: 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 4, pp. 3636–3641 (2003)Google Scholar
  8. 8.
    Oyama, A., Konolige, K., Cousins, S., Chitta, S., Conley, K., Bradski, G.: Come on in, our community is wide open for robotics research? In: The 27th Annual Conference of the Robotics Society of Japan, vol. 9 (2009)Google Scholar
  9. 9.
    Grisetti, G., Stachniss, C., Burgard, W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Transactions on Robotics 23(1), 34–46 (2007)CrossRefGoogle Scholar
  10. 10.
    Fox, D.: KLD-sampling: Adaptive particle filters and mobile robot localization. In: Advances in Neural Information Processing Systems, NIPS (2001)Google Scholar
  11. 11.
    Rosenthal, S., Biswas, J., Veloso, M.: An effective personal mobile robot agent through symbiotic human-robot interaction. In: 9th International Conference on Autonomous Agents and Multiagent Systems, pp. 915–922 (2010)Google Scholar
  12. 12.
    Van der Zant, T., Wisspeintner, T.: Robocup@ home: Creating and benchmarking tomorrows service robot applications. Robotic Soccer, 521–528 (2007)Google Scholar
  13. 13.
    Kam, M., Zhu, X., Kalata, P.: Sensor fusion for mobile robot navigation. Proceedings of the IEEE 85(1), 108–119 (1997)CrossRefGoogle Scholar
  14. 14.
    Biswas, J., Coltin, B., Veloso, M.: Corrective gradient refinement for mobile robot localization. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 73–78. IEEE (2011)Google Scholar
  15. 15.
    Biswas, J., Veloso, M.: Depth camera based indoor mobile robot localization and navigation. In: 2012 IEEE International Conference on Robotics and Automation, pp. 1697–1702 (2012)Google Scholar
  16. 16.
    Biswas, J., Veloso, M.: Wifi localization and navigation for autonomous indoor mobile robots. In: 2010 IEEE International Conference on Robotics and Automation, pp. 4379–4384 (2010)Google Scholar
  17. 17.
    Ferris, B., Hähnel, D., Fox, D.: Gaussian processes for signal strength-based location estimation. In: Proc. of Robotics Science and Systems. Citeseer (2006)Google Scholar
  18. 18.
    Rice, J.: The algorithm selection problem. Advances in Computers 15, 65–118 (1976)CrossRefGoogle Scholar
  19. 19.
    Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: Satzilla: portfolio-based algorithm selection for sat. Journal of Artificial Intelligence Research 32(1), 565–606 (2008)zbMATHGoogle Scholar
  20. 20.
    Smith-Miles, K.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys (CSUR) 41(1), 1–25 (2008)CrossRefGoogle Scholar
  21. 21.
    Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte carlo localization for mobile robots. In: 1999 IEEE International Conference on Robotics and Automation, vol. 2, pp. 1322–1328. IEEE (1999)Google Scholar
  22. 22.
    Lenser, S., Veloso, M.: Sensor resetting localization for poorly modelled mobile robots. In: IEEE International Conference on Robotics and Automation 2000, pp. 1225–1232 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Joydeep Biswas
    • 1
  • Manuela Veloso
    • 1
  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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