A Hybrid Framework For Attitude Estimation Of

Abstract

Inertial Measurement Unit (IMU) sensors are being actively used for motion tracking or attitude estimation due to their low weighted structure and cheap cost. IMU is a fusion of gyroscope, accelerometer, and magnetometer. There are many applications of IMU sensors. Some of which are gesture recognition, estimation of vehicular motion and orientation of objects, etc. Mostly gyroscope and accelerometer are used to estimate the attitude of any object where magnetometer provides the heading and course information. The gyroscope provides short-term stability because of inherited bias. Due to this bias an error starts to occur as the difference between actual measurement and expected measurement of the attitude tends to increase with time. This error can be large as 70 degrees/hour. This problem can be solved by using accelerometer in fusion with gyroscope. Accelerometer that measures accelerations acting on a body due to gravitation and other exterior forces. It is relatively easy to estimate attitude when there are no outliers but in presence of external interference its measurement is contaminated which creates jitters and lag in measured values. As the external forces are always present that’s why it never accurate. This is why we use these sensors in conjunction so; they can complement each other to give accurate results. To solve this problem, we have introduced an effective technique in which complementary filter is used to give accurate attitude estimate of the body in the presence of prolonged dynamic conditions and gyro bias. Two filters are used in cascade form where one filter deals with precise attitude estimation of the motion of a rigid body and external acceleration through synchronicity of accelerometer and gyroscope and the other filter deals with gyro bias estimation, this enables the proposed architecture to be used in a different application with minimum calibration. A novel threshold-based method is used for external acceleration detection so that external accelerations can be dealt efficiently. This will help in changing the confidence level on the measurements of accelerometer in case of prolonged accelerations and also compliments attitude estimation. Second filter is designed in a way that it estimates bias in the by using attitude estimation and accelerations profile and then the bias is removed from the system. The architecture is also compared with different estimators existing in the literature under different dynamical conditions on different datasets.