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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)

Abstract

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.

Keywords

Mobile Robot Unknown Environment World Coordinate System Occupancy Grid Robot Platform 
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.

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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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