Abstract




 
   

IJE TRANSACTIONS B: Applications Vol. 15, No. 2 (July 2002) 145-156   

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  A NEW APPROACH TO SELF-LOCALIZATION FOR MOBILE ROBOTS USING SENSOR DATA FUSION
 
 
B. Moshiri

Department of Electrical and Computer Engineering, Faculty of Engineering
University of Tehran, P.O.Box: 14395/515, Tehran, Iran, moshiri@ut.ac.ir

M. R. Asharif

Department of Information Engineering, Faculty of Engineering
University of Ryukyus, Okinawa, Japan, asharif@ie.u-ryukyu.ac.jp

R. HoseinNezhad

Department of Electrical and Computer Engineering, Faculty of Engineering
University of Tehran, P.O.Box: 14395/515, Tehran, Iran, rhnezha@ut.ac.ir

 
 
( Received: July 05, 2001 – Accepted: October 23, 2001 )
 
 

Abstract    This paper proposes a new approach for calibration of dead reckoning process. Using the well-known UMBmark (University of Michigan Benchmark) is not sufficient for a desirable calibration of dead reckoning. Besides, existing calibration methods usually require explicit measurement of actual motion of the robot. Some recent methods use the smart encoder trailer or long range finder sensors such as ultrasonic or laser range finders for automatic calibration. Manual measurement is necessary in the case of the robots that are not equipped with long-range detectors or such smart encoder trailer. Our proposed approach uses an environment map that is created by fusion of proximity data, in order to calibrate the odometry error automatically. In the new approach, the systematic part of the error is adaptively estimated and compensated by an efficient and incremental maximum likelihood algorithm. Actually, environment map data are fused with the odometry and current sensory data in order to acquire the maximum likelihood estimation. The advantages of the proposed approach are demonstrated in some experiments with Khepera robot. It is shown that the amount of pose estimation error is reduced by a percentage of more than 80%.

 

Keywords    Sensor Data Fusion, Pose Estimation, Dead Reckoning Calibration, Occupancy Grids, Maximum Likelihood, Map Building

 

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