UGV Localization Based on Fuzzy Logic and Extended Kalman Filtering

  • Athanasios Tsalatsanis
  • Kimon P. Valavanis
  • Ali Yalcin
Part of the Intelligent Systems, Control, and Automation: Science and Engineering book series (ISCA, volume 39)

Accurate localization necessitates precise sensor information regarding the vehicle's position and orientation. In general, sensors are vulnerable to environmental changes, disturbances, faults and noise to the communication channels. To cope with the uncertainty in sensor readings, data manipulation techniques such as preprocessing and sensor fusion have been commonly utilized. The dominant sensor fusion technique in the UGV localization area is that of Kalman filtering. Kalman filters are used to estimate the vehicle's position based on the vehicle's kinematics and sensor readings, assuming zero mean Gaussian noise to both vehicle's kinematics and sensor readings. A drawback of Kalman filtering techniques is the lack of information regarding the sensor performance in different operational environments: a vision system will provide more accurate information in an experiment that is conducted under daylight than an experiment conducted in a dark room. In this work, a multi-sensor localization method for UGVs based on fuzzy logic and Extended Kalman filtering is presented. This work utilizes a set of fuzzy logic controllers to incorporate the performance of a sensor under different operational conditions into the Extended Kalman filter. The sensors considered are a GPS, an IMU, a laser range finder, a stereo vision system and the vehicle's odometer. The superiority of the proposed filter methodology is demonstrated through extensive experimentation in various environmental conditions.


Global Position System Fuzzy Logic Mobile Robot Extend Kalman Filter Inertial Measurement Unit 
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 Science + Business Media B.V. 2009

Authors and Affiliations

  • Athanasios Tsalatsanis
    • 1
  • Kimon P. Valavanis
    • Ali Yalcin
      • 1
    1. 1.Department of Industrial and Management Systems EngineeringUniversity of South FloridaTampaUSA

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