基于多传感器的运动姿态测量算法
作者:
基金项目:

安徽省2015年度自然科学基金;国家科技支撑计划(2013BAH14F01)


Orientation Estimation Algorithm for Motion Based on Multi-Sensor
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [18]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    在基于MEMS传感技术的运动姿态测量中, 陀螺仪信号的漂移和载体线性加速度与重力加速度的叠加是影响测量结果准确性的主要原因, 实践中一般采用静态补偿和滤波技术减小测量误差. 基于自主研发的惯性测量单元, 设计了一种新型两级扩展卡尔曼滤波器: 基于四元数的运动姿态测量模型, 首先构造自适应加速度误差协方差矩阵, 消除载体线性加速度, 再采用多传感器融合技术进行数据融合, 修正陀螺仪信号漂移产生的误差. 实验表明, 本文算法结果与业界认可的动作捕捉系统Xsens的测量结果一致, 可有效满足应用需求.

    Abstract:

    In the motion orientation estimation based on MEMS sensor technology, gyroscope signal drift error and gravity superimposed with linear acceleration are the two major reasons affecting the accuracy of estimation. In practice, static compensation and filter technology are commonly used to reduce the orientation estimation error. This paper designs a novel double stage extend Kalman Filter performed on self-developed inertial measurement unit. Above all, we construct adaptive acceleration error covariance matrix to eliminate the linear acceleration in quaternion-based orientation estimation model. Then, in order to correct the drift error produced by gyroscope, the multi-sensor data fusion technology is adopted to fuse the data. Experiment result indicates that the performance of our algorithm is in accordance with the motion capture system Xsens approbated widely. It proves that the algorithm can meet the application requirements effectively.

    参考文献
    1 Moeslund TB, Granum E. A survey of computer vision-based human motion capture. Computer Vision and Image Understanding, 2001, 81(3): 231-268.
    2 Vlasic D, Adelsberger R, Vannucci G, et al. Practical motion capture in everyday surroundings. ACM Trans. on Graphics (TOG), 2007, 26(3): 35.
    3 Chan JCP, Leung H, Tang JKT, et al. A virtual reality dance training system using motion capture technology. IEEE Trans. on Learning Technologies, 2011, 4(2): 187-195.
    4 Mirabella O, Raucea A, Fisichella F, et al. A motion capture system for sport training and rehabilitation. 2011 4th International Conference on Human System Interactions (HSI). IEEE. 2011. 52-59.
    5 Kurihara K, Hoshino S, Yamane K, et al. Optical motion capture system with pan-tilt camera tracking and realtime data processing. ICRA. 2002. 1241-1248.
    6 Lee J, Ha I. Real-time motion capture for a human body using accelerometers. Robotica, 2001, 19(6): 601-610.
    7 Abbate N, Basile A, Brigante C, et al. Development of a MEMS based wearable motion capture system. 2009 2nd Conference on Human System Interactions. IEEE. 2009. 255-259.
    8 Rehbinder H, Hu X. Drift-free attitude estimation for accelerated rigid bodies. Automatica, 2004, 40(4): 653-659.
    9 Yun X, Lizarraga M, Bachmann ER, et al. An improved quaternion-based Kalman filter for real-time tracking of rigid body orientation. 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE. 2003, 2. 1074-1079.
    10 Madgwick SOH, Harrison AJL, Vaidyanathan R. Estimation of IMU and MARG orientation using a gradient descent algorithm.Rehabilitation 2011 IEEE International Conference on Robotics (ICORR). IEEE. 2011. 1-7.
    11 Sabatelli S, Galgani M, Fanucci L, et al. A double-stage Kalman filter for orientation tracking with an integrated processor in 9-D IMU. IEEE Trans. on Instrumentation and Measurement, 2013, 62(3): 590-598.
    12 Kuipers JB. Quaternions and rotation sequences. Princeton: Princeton University Press, 1999.
    13 Hoag D. Apollo guidance and Navigation: Considerations of apollo imu gimbal lock. Canbridge: MIT Instrumentation Laboratory, 1963: 1-64.
    14 Farrell J, Barth M. The global positioning system and inertial navigation. New York: McGraw-Hill, 1999.
    15 De Groote HF. On the complexity of quaternion multiplication. Information Processing Letters, 1975, 3(6): 177-179.
    16 Welch G, Bishop G. An introduction to the Kalman filter. 1995.
    17 Kalman filtering and neural networks. New York: Wiley, 2001.
    18 Roetenberg D, Luinge H, Slycke P. Xsens MVN: full 6DOF human motion tracking using miniature inertial sensors [Tech. Rep]. Xsens Motion Technologies BV, 2009.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

汪俊,许胜强,程楠,游永豪,张弦,唐正,杨先军.基于多传感器的运动姿态测量算法.计算机系统应用,2015,24(9):134-139

复制
分享
文章指标
  • 点击次数:1618
  • 下载次数: 3603
  • HTML阅读次数: 0
  • 引用次数: 0
历史
  • 收稿日期:2015-01-09
  • 最后修改日期:2015-03-04
  • 在线发布日期: 2015-09-14
文章二维码
您是第11227291位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号