Title: Position estimation for intelligent vehicles using an unscented Kalman filter

Authors: Haigui Xu, Chunxiang Wang, Ming Yang, Ruqing Yang

Addresses: The Research Institute of Robotics, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Shanghai 200240, PR China. ' The Research Institute of Robotics, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Shanghai 200240, PR China. ' The Department of Automation, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Shanghai 200240, PR China. ' The Research Institute of Robotics, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Shanghai 200240, PR China

Abstract: This paper proposes a data fusion method for intelligent vehicles, which integrates the data from odometry and magnetic sensors. Errors due to the wheels slippage or roughness of the ground will inevitably be accumulated during the dead-reckoning based on odometry. The magnetic sensors are used to provide absolute positioning reference. The data from odometry and the magnetic sensors are integrated by using an Unscented Kalman Filter (UKF). The UKF method for position estimation is compared with the Extended Kalman Filter (EKF) method. A field experiment is performed, and the results show that the UKF is more accurate in estimating the position of the intelligent vehicle than the EKF.

Keywords: unscented Kalman filter; UKF; position estimation; magnetic sensors; intelligent vehicles; data fusion; odometry sensors; magnetic sensors; sensor fusion; wheel slippage; ground roughness; dead reckoning; vehicle positioning.

DOI: 10.1504/IJVAS.2008.016485

International Journal of Vehicle Autonomous Systems, 2008 Vol.6 No.1/2, pp.186 - 194

Published online: 31 Dec 2007 *

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