Journal
MICROMACHINES
Volume 12, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/mi12111373
Keywords
portable mobile robot; quaternion implementation; complementary filter; extended Kalman filtering; attitude estimation
Categories
Funding
- National Key R&D Program of China [2018YFB1700100]
Ask authors/readers for more resources
In this study, a complementary filtering algorithm is used to address drift and noise in gyroscope and accelerometer, and high-precision attitude angles are obtained by combining IMU multi-sensor signals. Three different algorithms are utilized for attitude estimation, with results indicating that the Mahony complementary filtering algorithm is more suitable for low-cost embedded systems.
In robot inertial navigation systems, to deal with the problems of drift and noise in the gyroscope and accelerometer and the high computational cost when using extended Kalman filter (EKF) and particle filter (PF), a complementary filtering algorithm is utilized. By combining the Inertial Measurement Unit (IMU) multi-sensor signals, the attitude data are corrected, and the high-precision attitude angles are obtained. In this paper, the quaternion algorithm is used to describe the attitude motion, and the process of attitude estimation is analyzed in detail. Moreover, the models of the sensor and system are given. Ultimately, the attitude angles are estimated by using the quaternion extended Kalman filter, linear complementary filter, and Mahony complementary filter, respectively. The experimental results show that the Mahony complementary filtering algorithm has less computational cost than the extended Kalman filtering algorithm, while the attitude estimation accuracy of these two algorithms is similar, which reveals that Mahony complementary filtering is more suitable for low-cost embedded systems.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available