Research Article
A quaternion-based Attitude Estimate System Based on a Low Power Consumption Inertial Measurement Unit
@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
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.
Copyright © 2015 L. Liangju et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.