Please wait a minute...
Front. Inform. Technol. Electron. Eng.  2014, Vol. 15 Issue (7): 574-583    DOI: 10.1631/jzus.C1300302
    
A robust optical/inertial data fusion system for motion tracking of the robot manipulator
Jie Chen, Can-jun Yang, Jens Hofschulte, Wan-li Jiang, Cha Zhang
State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China; ABB Corporate Research China, Shanghai 201319, China
Download:   PDF(0KB)
Export: BibTeX | EndNote (RIS)      

Abstract  We present an optical/inertial data fusion system for motion tracking of the robot manipulator, which is proved to be more robust and accurate than a normal optical tracking system (OTS). By data fusion with an inertial measurement unit (IMU), both robustness and accuracy of OTS are improved. The Kalman filter is used in data fusion. The error distribution of OTS provides an important reference on the estimation of measurement noise using the Kalman filter. With a proper setup of the system and an effective method of coordinate frame synchronization, the results of experiments show a significant improvement in terms of robustness and position accuracy.

Key wordsData fusion      Optical tracking      Inertial measurement unit      Kalman filter     
Received: 23 October 2013      Published: 10 July 2014
CLC:  TP23  
Cite this article:

Jie Chen, Can-jun Yang, Jens Hofschulte, Wan-li Jiang, Cha Zhang. A robust optical/inertial data fusion system for motion tracking of the robot manipulator. Front. Inform. Technol. Electron. Eng., 2014, 15(7): 574-583.

URL:

http://www.zjujournals.com/xueshu/fitee/10.1631/jzus.C1300302     OR     http://www.zjujournals.com/xueshu/fitee/Y2014/V15/I7/574


基于光学摄像系统和惯性传感器数据融合的机器人运动跟踪系统

研究目的:机器人运动跟踪系统对测量精度、采样频率以及系统稳定性都有很高要求,目前市面上廉价的光学摄像运动跟踪系统难以满足。惯性传感器具有采样频率高、稳定性好等优点,但用于运动跟踪则会产生较大累计误差;它和光学摄像跟踪系统可以很好地互补。本文通过卡尔曼滤波算法将惯性传感器与光学摄像系统进行数据融合,以提升光学摄像系统的测量精度、采样频率以及稳定性,使其更好地用于机器人运动跟踪。
创新要点:提出了一种通过惯性传感器提升光学摄像系统性能的方法。基于对光学摄像系统性能的全面分析,提出了一个有针对性的系统实现方案。
方法提亮:将惯性传感器提供的加速度、角速度信息与光学摄像系统测得的位置、速度信息进行重力补偿和坐标同步处理后,运用卡尔曼滤波算法进行融合。通过分析光学摄像系统测量精度的不均匀分布情况,为卡尔曼滤波算法中测量噪声的估计提供了依据。
重要结论:解决了在系统实现过程中重力补偿、坐标同步以及测量噪声估计等问题。实验证实,通过数据融合,惯性传感器可以有效提高光学摄像系统的测量精度、采样频率以及稳定性。

关键词: 数据融合,  卡尔曼滤波,  光学摄像系统,  惯性传感器 
[1] Rong-Feng Zhang , Ting Deng , Gui-Hong Wang , Jing-Lun Shi , Quan-Sheng Guan . A robust object tracking framework based on a reliable point assignment algorithm[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(4): 545-558.
[2] Dong-wei Xu, Yong-dong Wang, Li-min Jia, Yong Qin, Hong-hui Dong. Real-time road traffic state prediction based on ARIMA and Kalman filter[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(2): 287-302.
[3] Xiao-ming Gou, Zhi-wen Liu, Wei Liu, You-gen Xu. Filtering and tracking with trinion-valued adaptive algorithms[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(8): 834-840.
[4] Mehdi Ahmadi Jirdehi, Reza Hemmati, Vahid Abbasi, Hedayat Saboori. A multi-functional dynamic state estimator for error validation: measurement and parameter errors and sudden load changes[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(11): 1218-1227.
[5] Peng-fei Qian, Guo-liang Tao, De-yuan Meng, Hao Liu. A modified direct adaptive robust motion trajectory tracking controller of a pneumatic system[J]. Front. Inform. Technol. Electron. Eng., 2014, 15(10): 878-891.
[6] Hong-ze Leng, Jun-qiang Song, Fu-kang Yin, Xiao-qun Cao. Notes and correspondence on ensemble-based three-dimensional variational filters[J]. Front. Inform. Technol. Electron. Eng., 2013, 14(8): 634-641.
[7] Jian Xu, Jian-xun Li, Sheng Xu. Quantized innovations Kalman filter: stability and modification with scaling quantization[J]. Front. Inform. Technol. Electron. Eng., 2012, 13(2): 118-130.
[8] Quan-bo Ge, Wen-bin Li, Cheng-lin Wen. SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(8): 615-628.
[9] Yuan-hui Zhang, Wei Wei, Dan Yu, Cong-wei Zhong. A tracking and predicting scheme for ping pong robot[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(2): 110-115.