Fuzzy Likelihood Estimation Based Map Matching for Mobile Robot Self-localization

  • Jinxia Yu
  • Zixing Cai
  • Zhuohua Duan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


Reliable self-localization is a key issue in mobile robot navigation techniques under unknown environment. Aimed at an experimental platform of mobile robot with two rocker-bogie suspensions and four drive wheels, the dead-reckoning error of the proprioceptive sensors (odometry, fiber optic gyros) and the ranging performance of the exteroceptive sensor (2D time of fight laser scanner) are analyzed in this paper. Then, the environmental map using occupancy grids is adopted to fuse the information of the robot’s pose by dead-reckoning method and the range to obstacles by laser scanner. In this condition, the map matching method, combined fuzzy logic and maximum likelihood estimation, is presented to improve mobile robot self-localization. By experiments of the robot platform, the effectiveness of this method is validated and the self-localization performance of mobile robot is enhanced.


Mobile Robot Unknown Environment World Coordinate System Occupancy Grid Robot Platform 
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  1. 1.
    Leonard, J.J., Durrant-Whyte, H.F.: Mobile Robot Localization by Tracking Geometric Beacons. IEEE Transactions on Robotics and Automation 7(5), 376–382 (1991)CrossRefGoogle Scholar
  2. 2.
    Lu, F., Milios, E.E.: Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1994), Seattle, WA, USA, pp. 935–938 (1994)Google Scholar
  3. 3.
    Cai, Z.X., He, H.G., Chen, H.: Some issues for mobile robot navigation under unknown environments (in Chinese). Control and Decision 17(4), 385–391 (2002)Google Scholar
  4. 4.
    Lu, F., Milios, E.E.: Globally Consistent Range Scan Alignment for Environment Mapping. Autonomous Robots 4(4), 333–349 (1997)CrossRefGoogle Scholar
  5. 5.
    Robin, R.M.: Introduction to AI Robotics, 1st edn. Publishing House of Electronics Industry, Beijing (2004)Google Scholar
  6. 6.
    Gasós, J., Rosetti, A.: Uncertainty representation for mobile robots: perception, modeling and navigation in unknown environments. Fuzzy Sets and Systems 107(1), 1–24 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jinxia Yu
    • 1
    • 2
  • Zixing Cai
    • 1
  • Zhuohua Duan
    • 1
    • 3
  1. 1.College of Information Science & EngineeringCentral South UniversityChangsha HunanChina
  2. 2.College of Computer Science & TechnologyHenan Polytechnic UniversityJiaozuo HenanChina
  3. 3.Department of Computer ScienceShaoguan UniversityShaoguan GuangdongChina

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