Advertisement

Seamless Indoor-Outdoor Robust Localization for Robots

  • P. UrcolaEmail author
  • M. T. Lorente
  • J. L. Villarroel
  • L. Montano
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 253)

Abstract

In this paper we present a unified localization technique for indoor-outdoor environments that allows a seamless transition between a mapped zone using laser rangefinder on-board sensors and a GPS based localization zone. Different situations are detected during the indoor-outdoor transitions, in which the sensors used change and the localization estimator has to manage them properly for a continuous localization. The quality in the GPS measurements and the zone where the robot is localized are used to determine the best instant for switching the localization parameters for adapting to the situations.

Keywords

Prediction Step Indoor Localization Robot Localization Continuous Localization Covariance Intersection 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT Press (2005)Google Scholar
  2. 2.
    Kang, J., Kim, D., Kim, E., Kim, Y., Yoo, S., Wi, D.: Seamless mobile robot localization service framework for integrated localization systems. In: 3rd International Symposium on Wireless Pervasive Computing (ISWPC), pp. 175–179 (May 2008)Google Scholar
  3. 3.
    Goel, P., Roumeliotis, S.I., Sukhatme, G.: Robust localization using relative and absolute position estimates. In: Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 1999, vol. 2, pp. 1134–1140 (1999)Google Scholar
  4. 4.
    Collier, J., Ramirez-Serrano, A.: Environment classification for indoor/outdoor robotic matching. In: Canadian Conference on Computer and Robot Vision, pp. 276–283 (2009)Google Scholar
  5. 5.
    Pacis, E.B., Sights, B., Ahuja, G., Kogut, G., Everett, H.R.: An adapting localization system for outdoor/indoor navigation. In: SPIE Proc. 6230: Unmanned Systems Technology VIII, Defense Security Symposium, Orlando, EEUU (April 2006)Google Scholar
  6. 6.
    Julier, S.J., Uhlmann, J.K.: Using covariance intersection for SLAM. Robotics and Autonomous Systems (55), 3–20 (2007)Google Scholar
  7. 7.
    Hentschel, M., Wulf, O., Wagner, B.: A gps and laser-based localization for urban and non-urban outdoor environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. 149–154 (2008)Google Scholar
  8. 8.
    Gu, D., El-Sheimy, N.: Heading accuracy improvement of mems imu/dgps integrated navigation system for land vehicle. In: Position, Location and Navigation Symposium, 2008 IEEE/ION, pp. 1292–1296 (2008)Google Scholar
  9. 9.
    Fox, D.: Adapting the sample size in particle filters through KLD-sampling. The International Journal of Robotics Research 22(12), 985–1003 (2003)CrossRefGoogle Scholar
  10. 10.
    Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with applications to tracking and navigation. John Wiley & Sons, Inc. (2001)Google Scholar
  11. 11.
    Minguez, J.: The obstacle-restriction method for robot obstacle avoidance in difficult environments. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005), pp. 2284–2290 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • P. Urcola
    • 1
    Email author
  • M. T. Lorente
    • 1
  • J. L. Villarroel
    • 1
  • L. Montano
    • 1
  1. 1.Instituto de Investigación en Ingeniería de AragónUniversidad de ZaragozaZaragozaSpain

Personalised recommendations