Localization based on the Hybrid Extended Kalman Filter with a highly accurate odometry model of a mobile robot

This paper describes an improving method for solving localization problems with a highly accurate model of a mobile robot either in an uncertainly large-scale environment. Firstly, we motivate our approach by analyzing intensively the dead-reckoning model for the tricycle robot type. Secondly, we propose the localization algorithm based on a hybrid extended Kalman filter using artificial beacons. In this paper, 3600 sensor scan is used for each observation and the odometry data is updated to estimate the robot position. Then a comparison between the real and the estimated location of beacons and analyzing of the filterpsilas performance are taken. The simulation results show that the proposed algorithm can lead the robot to robustly navigate in uncertain environments.

[1]  Kostas J. Kyriakopoulos,et al.  Simultaneous localization and map building for mobile robot navigation , 1999, IEEE Robotics Autom. Mag..

[2]  Doo-Sung Ahn,et al.  Map building and localization on autonomous mobile robot using graph and fuzzy inference system , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[3]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[4]  Agostino Martinelli,et al.  The odometry error of a mobile robot with a synchronous drive system , 2002, IEEE Trans. Robotics Autom..

[5]  Hichem Maaref,et al.  Sensor-based navigation of a mobile robot in an indoor environment , 2002, Robotics Auton. Syst..

[6]  Johann Borenstein,et al.  Sensor fusion for mobile robot dead-reckoning with a precision-calibrated fiber optic gyroscope , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[7]  Lindsay Kleeman,et al.  Accurate odometry and error modelling for a mobile robot , 1997, Proceedings of International Conference on Robotics and Automation.

[8]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.