LAUSR

The initial alignment provides an accurate attitude for strapdown inertial navigation system (SINS). By further estimating the IMU's bias and misalignment angle, the recursive Bayesian filter is accurate. However, the prior heading error has significant influence on the convergence speed and accuracy. In addition, the accuracy will be limited by its iteration at a single time-step. Coarse alignment method optimization-based alignment uses maximum likelihood estimation (MLE) to find the optimal attitude quickly. However, few methods consider the IMU bias and misalignment angle, which will reduce the attitude accuracy. In this article, a unified method based on factor graph optimization (FGO) and inertial base frame (IBF) is proposed. The attitude is estimated by MLE, IMU bias, and misalignment angle are estimated by MAP estimation. The state of all time steps is optimized together to further improve the accuracy. Physical experiments on the rotation MEMS SINS show that the heading accuracy of this method is improved in limited alignment time.