Journal of Intelligent & Robotic Systems

, Volume 62, Issue 2, pp 187–203 | Cite as

EKF-Based Localization of a Wheeled Mobile Robot in Structured Environments

  • Luka TeslićEmail author
  • Igor Škrjanc
  • Gregor Klančar


This paper deals with the problem of mobile-robot localization in structured environments. The extended Kalman filter (EKF) is used to localize the four-wheeled mobile robot equipped with encoders for the wheels and a laser-range-finder (LRF) sensor. The LRF is used to scan the environment, which is described with line segments. A prediction step is performed by simulating the kinematic model of the robot. In the input noise covariance matrix of the EKF the standard deviation of each robot-wheel’s angular speed is estimated as being proportional to the wheel’s angular speed. A correction step is performed by minimizing the difference between the matched line segments from the local and global maps. If the overlapping rate between the most similar local and global line segments is below the threshold, the line segments are paired. The line parameters’ covariances, which arise from the LRF’s distance-measurement error, comprise the output noise covariance matrix of the EKF. The covariances are estimated with the method of classic least squares (LSQ). The performance of this method is tested within the localization experiment in an indoor structured environment. The good localization results prove the applicability of the method resulting from the classic LSQ for the purpose of an EKF-based localization of a mobile robot.


Mobile robot Localization Extended Kalman Filter Covariance matrix Line feature 


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  1. 1.
    Anousaki, G.C., Kyriakopoulos, K.J.: Simultaneous localization and map building of skid-steered robots. IEEE Robot. Autom. Mag. 14(1), 79–89 (2007)CrossRefGoogle Scholar
  2. 2.
    Arras, K.O., Siegwart, R.Y.: Feature extraction and scene interpretation for map-based navigation and map building. In: Proceedings of SPIE, Mobile Robotics XII, vol. 3210, pp. 42–53 (1997)Google Scholar
  3. 3.
    Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping (SLAM): part II. IEEE Robot. Autom. Mag. 13(3), 108–117 (2006)CrossRefGoogle Scholar
  4. 4.
    Baltzakis, H., Trahanias, P.: Hybrid mobile robot localization using switching state-space models. In: IEEE International Conference on Robotics and Automation, 2002. Proceedings. ICRA ’02, pp. 366–373 (2002)Google Scholar
  5. 5.
    Blažič, S., Škrjanc, I., Gerkšič, S., Dolanc, G., Strmčnik, S., Hadjiski, M.B., Stathaki, A.: Online fuzzy identification for an intelligent controller based on a simple platform. Eng. Appl. Artif. Intell. 22(4-5), 628–638 (2009)CrossRefGoogle Scholar
  6. 6.
    Blažič, S., Škrjanc, I., Matko, D.: Globally stable direct fuzzy model reference adaptive control. Fuzzy Sets Syst. 139(1), 3–33 (2003)CrossRefzbMATHGoogle Scholar
  7. 7.
    Borges, G.A., Aldon, M.-J.: A split-and-merge segmentation algorithm for line extraction in 2-D range images. In: Proceedings of the International Conference on Pattern Recognition, vol. 1, pp. 1441 (2000)Google Scholar
  8. 8.
    Borges, G.A., Aldon, M.-J.: Line extraction in 2D range images for mobile robotics. J. Intell. Robot. Syst. 40(3), 267–297 (2004)CrossRefGoogle Scholar
  9. 9.
    Choi, Y.-H., Lee, T.-K., Oh, S.-Y.: A line feature based SLAM with low grade range sensors using geometric constraints and active exploration for mobile robot. Auton. Robots 24(1), 13–27 (2008)CrossRefGoogle Scholar
  10. 10.
    Crowley, J.L., Wallner, F., Schiele, B.: Position estimation using principal components of range data. In: 1998 IEEE International Conference on Robotics and Automation, 1998. Proceedings, pp. 3121–3128 (1998)Google Scholar
  11. 11.
    Diosi, A., Kleeman, L.: Laser scan matching in polar coordinates with application to SLAM. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005), pp. 3317–3322 (2005)Google Scholar
  12. 12.
    Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)CrossRefGoogle Scholar
  13. 13.
    Forsberg, J., Larsson, U., Ahman, P., Wernersson, A.: The Hough transform inside the feedback loop of a mobile robot. In: IEEE International Conference on Robotics and Automation, 1993. Proceedings, vol. 1, pp. 791–798 (1993)Google Scholar
  14. 14.
    Garulli, A., Giannitrapani, A., Rossi, A., Vicino, A.: Mobile robot SLAM for line-based environment representation. In: 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC ’05, pp. 2041–2046 (2005)Google Scholar
  15. 15.
    Garulli, A., Giannitrapani, A., Rossi, A., Vicino, A.: Simultaneous localization and map building using linear features. In: Proceedings of the 2nd European Conference on Mobile Robots, pp. 44–49 (2005)Google Scholar
  16. 16.
    Giesler, B., Graf, R., Dillmann, R., Weiman, C.F.R.: Fast mapping using the log-Hough transformation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 1998. Proceedings, vol. 3, pp. 1702–1707 (1998)Google Scholar
  17. 17.
    Jensfelt, P., Christensen, H.I.: Pose tracking using laser scanning and minimalistic environmental models. IEEE Trans. Robot. Autom., 17(2), 138–147 (2001)CrossRefGoogle Scholar
  18. 18.
    Latecki, L.J., Lakaemper, R., Sun, X, Wolter, D.: Building polygonal maps from laser range data. In: ECAI Int. Cognitive Robotics Workshop, Valencia, Spain, August 2004 (2004)Google Scholar
  19. 19.
    Nguyen, V., Martinelli, A., Tomatis, N., Siegwart, R.: A comparison of line extraction algorithms using 2D laser rangefinder for indoor mobile robotics. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005), pp. 1929–1934 (2005)Google Scholar
  20. 20.
    Pfister, S.T., Roumeliotis, S.I., Burdick, J.W.: Weighted line fitting algorithms for mobile robot map building and efficient data representation. In: IEEE International Conference on Robotics and Automation, 2003. Proceedings. ICRA ’03, vol. 1, pp. 1304–1311 (2003)Google Scholar
  21. 21.
    Pozna, C., Troester, F., Precup, R.-E., Tar, J.K., Preitl, S.: On the design of an obstacle avoiding trajectory: method and simulation. Math. Comput. Simul. 79(7), 2211–2226 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  22. 22.
    Rofer, T.: Using histogram correlation to create consistent laser scan maps. In: IEEE/RSJ International Conference on Intelligent Robots and System, 2002, vol. 1, pp. 625–630 (2002)Google Scholar
  23. 23.
    Schiele, B., Crowley, J.L.: A comparison of position estimation techniques using occupancy grids. In: IEEE Conference on Robotics and Autonomous Systems, 1994, (ICRA 94) (1994)Google Scholar
  24. 24.
    Teslić, L., Škrjanc, I., Klančar, G.: Using a LRF sensor in the Kalman-filtering-based localization of a mobile robot. ISA Trans. 49(1), 145–153 (2010)CrossRefGoogle Scholar
  25. 25.
    Thrun, S.: Robotic mapping: a survey. In: Lakemeyer, G., Nebel, B. (eds.) Exploring Artificial Intelligence in the New Millennium. Morgan Kaufmann, San Francisco (2002)Google Scholar
  26. 26.
    Tomatis, N., Nourbakhsh, I., Siegwart, R.: Hybrid simultaneous localization and map building: a natural integration of topological and metric. Robot. Auton. Syst. 44(1), 3–14 (2003)CrossRefGoogle Scholar
  27. 27.
    Veeck, M., Veeck, W.: Learning polyline maps from range scan data acquired with mobile robots. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004. (IROS 2004). Proceedings, vol. 2, pp. 1065–1070 (2004)Google Scholar
  28. 28.
    Yan, Z., Shubo, T., Lei, L., Wei, W.: Mobile robot indoor map building and pose tracking using laser scanning. In: International Conference on Intelligent Mechatronics and Automation, 2004. Proceedings, pp. 656–661 (2004)Google Scholar
  29. 29.
    Yaqub, T., Tordon, M.J., Katupitiya, J.: Line segment based scan matching for concurrent mapping and localization of a mobile robot. In: 9th International Conference on Control, Automation, Robotics and Vision, 2006. (ICARCV ’06), pp. 1–6 (2006)Google Scholar
  30. 30.
    Zhang, X., Rad, A.B., Wong, Y.-K.: A robust regression model for simultaneous localization and mapping in autonomous mobile robot. J. Intell. Robot. Syst. 53(2), 183–202 (2008)CrossRefGoogle Scholar

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© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringUniversity of LjubljanaLjubljanaSlovenia

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