An adaptive extended Kalman filter for attitude estimation of Self-Balancing Two-Wheeled Robot

An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.

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EPE and Speed Adaptive Extended Kalman Filter for Vehicle Position and Attitude Estimation with Low Cost GNSS and IMU Sensors

Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics ◽

10.5220/0005023706490656 ◽

2014 ◽

Cited By ~ 5

Author(s):

P. Balzer ◽

T. Trautmann ◽

O. Michler

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Low Cost ◽

Attitude Estimation ◽

Vehicle Position ◽

Adaptive Extended Kalman Filter

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A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Sensors ◽

10.3390/s20030803 ◽

2020 ◽

Vol 20 (3) ◽

pp. 803 ◽

Cited By ~ 5

Author(s):

Ákos Odry ◽

Istvan Kecskes ◽

Peter Sarcevic ◽

Zoltan Vizvari ◽

Attila Toth ◽

...

Keyword(s):

Kalman Filter ◽

Real Time ◽

Extended Kalman Filter ◽

Attitude Estimation ◽

Sensor Data ◽

External Disturbances ◽

Filter Performance ◽

Filter Structure ◽

Adaptive Extended Kalman Filter ◽

Fuzzy Adaptive

This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.

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