ew 16(9): e5

Research Article

A quaternion-based Attitude Estimate System Based on a Low Power Consumption Inertial Measurement Unit

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  • @ARTICLE{10.4108/eai.14-10-2015.2261661,
        author={yunkun ning and liangju li and guoru zhao and Yingnan Ma and Xing Gao and Zongzhen Jin},
        title={A quaternion-based Attitude Estimate System Based on a Low Power Consumption Inertial Measurement   Unit},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={3},
        number={9},
        publisher={ACM},
        journal_a={EW},
        year={2015},
        month={12},
        keywords={attitude estimate ; kalman filter ; digital motion processor ; mpu9150 ; quaternion-based},
        doi={10.4108/eai.14-10-2015.2261661}
    }
    
  • yunkun ning
    liangju li
    guoru zhao
    Yingnan Ma
    Xing Gao
    Zongzhen Jin
    Year: 2015
    A quaternion-based Attitude Estimate System Based on a Low Power Consumption Inertial Measurement Unit
    EW
    EAI
    DOI: 10.4108/eai.14-10-2015.2261661
yunkun ning1, liangju li2,*, guoru zhao1, Yingnan Ma3, Xing Gao3, Zongzhen Jin3
  • 1: Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences
  • 2: Wuhan Research Institute of Post and Telecommunications
  • 3: Beijing Research Center of Urban System Engineering
*Contact email: fordream03@gmail.com

Abstract

Accurate and real-time tracking of the orientation or attitude of rigid bodies has traditional applications in robotics, aerospace, underwater vehicles, human body motion capture, etc. Towards human body motion capture, especially wearable devices, the use of a longer time has always been a challenge for several weeks or several months continuously, so a low-cost chip and a low computational cost algorithm are necessary .The paper presented a quaternion-based algorithm that integrated the sensor output with the Kalman filtering algorithm, and a low power consumption Inertial Measurement Unit (IMU) for the attitude estimation. The low power consumption IMU with an inner Digital Motion Processor(DMP) from InvenSense Inc. called MPU9150, which contains triaxial accelerometers, triaxial gyroscopes, triaxial magnetometers and inner DMP. Firstly, we got attitude quaternion from DMP, and used the factored quaternion algorithm (FQA) to calculate course angle quaternion component. Then the Kalman Filtering algorithm was used to mix them together to acquire the accurate and good real-time performance attitude .The experimental results showed that Kalman filtering algorithm to mix DMP output and magnetometers data have better performance than gradient descent algorithm and complementary filter algorithm even in static performance and dynamic performance and power consumption.