Odometry using wheel encoders is one of the fundamental techniques for the pose estimation of wheeled mobile robots. However, odometry has a drawback that the position errors are accumulated when the travel distance increases. Therefore, position errors are required to be reduced using appropriate calibration schemes. The UMBmark method is the one of the widely used calibration schemes for two wheel differential drive robots. In UMBmark method, it is assumed that odometry error sources are independent. However, there is coupled effect of odometry error sources. In this paper, a new calibration scheme by considering the coupled effect of error sources is proposed. We also propose the test track design for the proposed calibration scheme. The numerical simulation and experimental results show that the odometry accuracy can be improved by the proposed calibration scheme.

Recently, automatic parking assist systems are commercially available in some cars. In order to improve the reliability and the accuracy of parking control, pose uncertainty of a vehicle and some experimental issues should be solved. In this paper, following three schemes are proposed. (1) Odometry calibration scheme for the Car-Like Mobile Robot.(CLMR) (2) Accurate localization using Extended Kalman Filter(EKF) based redundant odometry fusion. (3) Trajectory tracking controller to compensate the tracking error of the CLMR. The proposed schemes are experimentally verified using a miniature Car-Like Mobile Robot. This paper shows that odometry accuracy and trajectory tracking performance can be dramatically improved by using the proposed schemes.