JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE C

Accepted manuscript available online (unedited version)

A robust optical/inertial data fusion system for motion tracking of the robot manipulator

Author(s): Jie Chen, Can-jun Yang, Jens Hofschulte, Wan-li Jiang, Cha Zhang
Affiliation(s): State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China; more
Corresponding email(s): chenjie.zju@gmail.com, ycj@zju.edu.cn
Key Words: Data fusion, Optical tracking, Inertial measurement unit, Kalman filter

Share this article to： More <<< Previous Paper \|Next Paper >>>

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[J]. Journal of Zhejiang University Science C,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.C1300302

@article{title="A robust optical/inertial data fusion system for motion tracking of the robot manipulator",
author="Jie Chen, Can-jun Yang, Jens Hofschulte, Wan-li Jiang, Cha Zhang",
journal="Journal of Zhejiang University Science C",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.C1300302"
}

%0 Journal Article
%T A robust optical/inertial data fusion system for motion tracking of the robot manipulator
%A Jie Chen
%A Can-jun Yang
%A Jens Hofschulte
%A Wan-li Jiang
%A Cha Zhang
%J Journal of Zhejiang University SCIENCE C
%P 574-583
%@ 2095-9184
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/jzus.C1300302"

TY - JOUR
T1 - A robust optical/inertial data fusion system for motion tracking of the robot manipulator
A1 - Jie Chen
A1 - Can-jun Yang
A1 - Jens Hofschulte
A1 - Wan-li Jiang
A1 - Cha Zhang
J0 - Journal of Zhejiang University Science C
SP - 574
EP - 583
%@ 2095-9184
Y1 - in press
PB - Zhejiang University Press & Springer
ER -
doi="https://doi.org/10.1631/jzus.C1300302"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

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.

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。
数据融合；卡尔曼滤波；光学摄像系统；惯性传感器

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Aristidou, A., Cameron, J., Lasenby, J., 2008. Real-time estimation of missing markers in human motion capture. 2nd Int. Conf. on Bioinformatics and Biomedical Engineering, p.1343-1346.

[2]Bleser, G., Stricker, D., 2009. Advanced tracking through efficient image processing and visual–inertial sensor fusion. Comput. & Graph., 33(1):59-72.

[3]Caron, F., Duflos, E., Pomorski, D., et al., 2006. GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects. Inform. Fus., 7(2):221-230.

[4]Claasen, G.C., Martin, P., Picard, F., 2011. Tracking and control for handheld surgery tools. IEEE Biomedical Circuits and Systems Conf., p.428-431.

[5]Dong, Y., Zwahlen, P., Nguyen, A.M., et al., 2010. High performance inertial navigation grade sigma-delta MEMS accelerometer. IEEE/ION Position Location and Navigation Symp., p.32-36.

[6]Foxlin, E., Altshuler, Y., Naimark, L., et al., 2004. Flighttracker: a novel optical/inertial tracker for cockpit enhanced vision. Proc. 3rd IEEE/ACM Int. Symp. on Mixed and Augmented Reality, p.212-221.

[7]Park, I., Lee, B., Cho, S., et al., 2012. Laser-based kinematic calibration of robot manipulator using differential kinematics. IEEE/ASME Trans. Mechatron., 17(6):1059-1067.

[8]Ren, H., Rank, D., Merdes, M., et al., 2012. Multisensor data fusion in an integrated tracking system for endoscopic surgery. IEEE Trans. Inform. Technol. Biomed., 16(1):106-111.

[9]Shirinzadeh, B., Teoh, P., Tian, Y., et al., 2010. Laser interferometry-based guidance methodology for high precision positioning of mechanisms and robots. Robot. Comput.-Integr. Manuf., 26(1):74-82.

[10]Soroush, A., Farahmand, F., Salarieh, H., 2012. Design and implementation of an improved real-time tracking system for navigation surgery by fusion of optical and inertial tracking methods. Appl. Mech. Mater., 186:273-279.

[11]Su, L., Shi, L., Yu, Y., 2009. Collaborative assembly operation between two modular robots based on the optical position feedback. J. Robot., Article ID 214154.

[12]Sukkarieh, S., Nebot, E.M., Durrant-Whyte, H.F., 1999. A high integrity IMU/GPS navigation loop for autonomous land vehicle applications. IEEE Trans. Robot. Autom., 15(3):572-578.

[13]Syed, Z.F., Aggarwal, P., Niu, X., et al., 2008. Civilian vehicle navigation: required alignment of the inertial sensors for acceptable navigation accuracies. IEEE Trans. Veh. Technol., 57(6):3402-3412.

[14]Tao, Y., Hu, H., 2008. A novel sensing and data fusion system for 3-D arm motion tracking in telerehabilitation. IEEE Trans. Instrum. Meas., 57(5):1029-1040.

[15]Wiles, A.D., Thompson, D.G., Frantz, D.D., 2004. Accuracy assessment and interpretation for optical tracking systems. SPIE, 5367:421-432.

[16]Xiong, N., Svensson, P., 2002. Multi-sensor management for information fusion: issues and approaches. Inform. Fus., 3(2):163-186.

[17]Yun, X., Calusdian, J., Bachmann, E.R., et al., 2012. Estimation of human foot motion during normal walking using inertial and magnetic sensor measurements. IEEE Trans. Instrum. Meas., 61(7):2059-2072.

[18]Zhang, P., Gu, J., Milios, E.E., et al., 2005. Navigation with IMU/GPS/digital compass with unscented Kalman filter. IEEE Int. Conf. on Mechatronics and Automation, p.1497-1502.

[19]Zhang, Z.Q., Wu, J.K., 2011. A novel hierarchical information fusion method for three-dimensional upper limb motion estimation. IEEE Trans. Instrum. Meas., 60(11):3709-3719.

[20]Zhou, H., Hu, H., 2010. Reducing drifts in the inertial measurements of wrist and elbow positions. IEEE Trans. Instrum. Meas., 59(3):575-585.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

- Go to

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

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference