Quadcopter Attitude Estimation using filters for Sensor Fusion in 6D Inertial Measurement Unit
AbstractOrientation tracking of a quadcopter Unmanned Aerial Vehicle (UAV) involves monitoring the Roll, Pitch and Yaw angles. These angles provide feedback information that is then used to give appropriate angling and heading orientation. Measurements of these Euler angles is accomplished by use of an Inertial Measurement Unit (IMU) consisting of either a gyroscope, accelerometer or both. The IMU created with the gyroscope is less sensitive to vibrations and is not affected by earth’s gravity. One of the problems that a gyro based IMU encounters is the drifting of the angles. Another problem occurs when the IMU is started at an angled surface. This is because the IMU has no reference to what is level. In static or slow movement, the accelerometer measures roll and pitch by leveling to correct the gyro-unbounded error. This is due to the trustworthiness of the gravitational measurement. While the accelerometer gives absolute measurement of the quadcopter attitude, the motors on the quadcopter produce a lot of vibrations introducing significant noise into the accelerometer reading. Therefore, a proper fusion of IMU data is needed to overcome the shortcomings of each sensor. Kalman filter is therefore proposed to merge the two sensor measurements to achieve better estimates, redundancy and drift compensation. In conclusion, the performance of the Kalman filter is then compared with that of the unfiltered sensor data and Complimentary filter.
Keywords— Accelerometer, Complimentary filter, Gyroscope, Kalman filter,
Jun 22, 2018
How to Cite
OLOO, Jackson O.; KAMAU, Stanley I.. Quadcopter Attitude Estimation using filters for Sensor Fusion in 6D Inertial Measurement Unit. Proceedings of Sustainable Research and Innovation Conference, [S.l.], p. 41-44, june 2018. ISSN 2079-6226. Available at: <http://sri.jkuat.ac.ke/ojs/index.php/proceedings/article/view/659>. Date accessed: 26 may 2019.
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