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

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Abstract

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.

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References

  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)

    Article  Google Scholar 

  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)

  3. Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping (SLAM): part II. IEEE Robot. Autom. Mag. 13(3), 108–117 (2006)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  6. Blažič, S., Škrjanc, I., Matko, D.: Globally stable direct fuzzy model reference adaptive control. Fuzzy Sets Syst. 139(1), 3–33 (2003)

    Article  MATH  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

  12. Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)

    Article  Google Scholar 

  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)

  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)

  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)

  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)

  17. Jensfelt, P., Christensen, H.I.: Pose tracking using laser scanning and minimalistic environmental models. IEEE Trans. Robot. Autom., 17(2), 138–147 (2001)

    Article  Google Scholar 

  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)

  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)

  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)

  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)

    Article  MATH  MathSciNet  Google Scholar 

  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)

  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)

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

  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)

  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)

  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)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000, Ljubljana, Slovenia

    Luka Teslić, Igor Škrjanc & Gregor Klančar

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  1. Luka Teslić
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  2. Igor Škrjanc
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  3. Gregor Klančar
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Correspondence to Luka Teslić.

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Teslić, L., Škrjanc, I. & Klančar, G. EKF-Based Localization of a Wheeled Mobile Robot in Structured Environments. J Intell Robot Syst 62, 187–203 (2011). https://doi.org/10.1007/s10846-010-9441-8

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  • DOI: https://doi.org/10.1007/s10846-010-9441-8

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