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.
