Online Misalignment Estimation of Strapdown Navigation for Land Vehicle under Dynamic Condition

Abstract

In recent times, localization and positioning techniques have rapidly developed with the increasing demand for unmanned vehicles. Most positioning systems for land vehicles based on GPS-IMU, use a non-holonomic constraint to determine misalignment between sensor and vehicle body frame; however, misalignment estimation depending on non-holonomic constraint has limitations in high speed environments and there is a lack of observability for roll misalignment. This paper suggests an online misalignment estimation method under dynamic conditions that violates the non-holonomic constraint. It provides roll, pitch and yaw misalignment angles of IMU mounted on a vehicle, and corresponding sideslip angle of the vehicle at the position of IMU. The misalignment estimator is designed as a linear error state Kalman filter, which takes the results of a strapdown inertial navigation working simultaneously. Computer simulations and real environment experiments with consumer grade GPS and MEMS IMU are performed to demonstrate the performance and reliability of the given method.