HUANG Liang-song, GUO Xiao-li, LI Yu-xia
(College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China)
Abstract： Self-localization is a fundamental requirement for the mobile robot. Robot usually contains a large number of different sensors, which provide the information of robot localization, and all the sensor information should be considered for the optimal location. Kalman filter is efficient to realize the information fusion. Used as an efficient sensor fusion algorithm, Kalman filter is an advanced filtering technique which can reduce errors of the position and orientation of the sensors. Kalman filter has been paied much attention to robot automation and solutions to solve uncertainties such as robot localization, navigation, following, tracking, motion control, estimation and prediction. The paper briefly describes Kalman filter theory, and establishes a simple mathematical model based on muti-sensor mobile robot. Meanwhile, Kalman filter is used in robot self-localization by simulations, and it is demonstrated by simulations that Kalman filter is effective.
Key words： Kalman filter; mobile robot; self-localization; target orientation
CLD number： TP249 Document code： A
Article ID： 1674-8042(2014)02-0052-03 doi： 10.3969/j.issn.1674-8042.2014.02.010
References
[1] CHEN Rong-bao, ZHAO He, XIAO Ben-xiao. Self-localization of mobile robot based on monocular and extended kalman filter. Electronic Measurement & Instruments, ICEMI '09. 9th International Conference, 2009: 450-454.
[2] Chen G, Chui C K. Kalman filtering: with real-time applications. New York: Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 2nd edition, 1991.
[3] LI Tie-zhu. Study on the method of obstacle avoidance of robot navigation and localization. Tiaojin: Northeast Dianli University, 2006.
[4] WANG Xiao-dong, CHEN Rong, LIU Jun-s. Monte carlo signal processing for wireless communications/ Journal of VLSI Signal Processing, 2002, 30: 89-105.
[5] Kalman R E. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 1960, 82(1): 35-45.
[6] Mortari D, Markley F L, Singla P. Optimal linear attiude estimator. Journal of Guidance, Control and Dynamics, 2007, 30(6): 1619-1627.
[7] ZHOU Hua. Application of multi-sensor fusion technology in the localization of mobile robot. Wuhan: Wuhan University of Technology, 2009.
[8] QIN Yong-yuan, ZHANG Hong-yue, WANG Shu-hua. Kalman filter and the principle of integrated navigation. Xi’an:Northwestern Polytechnical University Press, 2004.
[9] CAO Qi-xin, ZHANG Lei. Wheeled autonomous mobile robot. Shanghai: Shanghai Jiao Tong University Press, 2012.
[10] FU Meng-yin, DENG Zhi-hong, ZHANG Zhi-wei. Application of Kalman filtering theory and its application in navigation system. Beijing: Science Press, 2003.
卡尔曼滤波器在移动机器人自主定位中的应用
黄梁松, 郭小丽, 李玉霞
(山东科技大学 电气与自动化工程学院， 山东 青岛 266590 )
摘要:自主定位是移动机器人的基本性能， 但移动机器人最优的定位信息需要综合考虑所携带的各类传感器数据， 故如何有效融合这些数据是自主定位的难点。 卡尔曼滤波器是实现不同信息融合的工具， 可有效减少定位过程中机器人的位置和角度误差， 因此在机器人定位、 导航、 后跟踪、 运动控制\, 评估、 预测等方面得到了广泛应用。 本文阐述了卡尔曼滤波器的基本原理， 建立了一个基于多传感器移动机器人的简易数学模型， 并通过仿真实现了目标定位的基本功能， 结果验证了卡尔曼滤波器自主定位移动机器人的可行性。
关键词:卡尔曼滤波器； 移动机器人； 自主定位； 目标定向
引用格式：HUANG Liang-song, GUO Xiao-li, LI Yu-xia. Application of Kalman filter on mobile robot self-localization. Journal of Measurement Science and Instrumentation, 2014, 5(2): 52-54. [doi: 10.3969/j.issn.1674-8042.2014.02.010]
[full text view]
