Dynamic Sensor Bias Correction for Attitude Estimation Using Unscented Kalman Filter in Autonomous Vehicle

Abstract. This paper describes a method for estimating sensor biases by using a lowdimensional Unscented Kalman Filter (UKF) to maintain the positional estimation accuracy of an autonomous vehicle (AV). It is difficult to estimate attitude accurately in a blind situation (such as with no GPS satellites and no landmarks), because of sensor bias. We developed a dead reckoning system for an embedded system using the UKF. The UKF has high computational effort, so, we decreased the number of dimensions in the UKF by excluding sensor biases term. On the presumption that AV drives steadily, we derived equations for the relationship between the averages of angular acceleration and gyro bias, and corrected the sensor output. Instead of using high-dimensional UKF, we corrected sensor biases by using these equations. This method quickly and accurately estimated attitude.