Alleviating the Magnetic Effects on Magnetometers using Vehicle Kinematics for Yaw Estimation for Autonomous Ground Vehicles 2020-01-1025
Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement. The present work focuses on alleviating magnetic disturbances for ground vehicles by fusing the vehicle kinematics information with IMU senor in an Extended Kalman filter (EKF) with the vehicle orientation represented using Quaternions. In addition, the error in rate measurements from gyro sensor gets accumulated as the time progress which results in drift in rate measurements and thus affecting the vehicle orientation estimation. To resolve and account for the gyro drift, the EKF algorithm includes gyro bias terms in state vector, which augments the state vector with 4 Quaternions and 3 gyro bias vectors. The proposed modified EKF strategy has been experimentally tested and validated on 1/5th scale buggy type truck. The developed EKF, analysis and results are present which shows that, while the vehicle is affected by up to 1.2 nT magnetic strength and based on the curvature of the road it reduces the absolute error in yaw estimations to 7 % in straight path and 11% during tuning paths. Due to high accuracy in speed sensor and steering angle measurements, this fusion algorithm is robust and can make yaw estimations within ±1% error for about 30 meter distance.
Ahammad Basha Dudekula, Jeffrey D. Naber