A Novel Unified Self-alignment Method of SINS Based on FGO

The self-alignment process can provide an accurate initial attitude of SINS. The conventional two-procedure method usually includes coarse and fine alignment processes. Coarse alignment is usually based on the OBA (optimization-based alignment) method, batch estimates the constant initial attitude at the beginning of self-alignment. OBA converges rapidly, however, the accuracy is low because the method doesn't consider IMU's bias errors. The fine alignment applies a recursive Bayesian filter which makes the system error estimation of the IMU more accurate, but at the same time, the attitude error converges slowly with a large heading misalignment angle. Researchers have proposed the unified self-alignment to achieve self-alignment in one procedure, but when the misalignment angle is large, the existing methods based on recursive Bayesian filter are still slow to converge. In this paper, a unified method based on batch estimator FGO (factor graph optimization) is raised. To the best as the author known, this is the first batch method capable of estimating all the systematic errors of IMU and the constant initial attitude simultaneously, with fast convergence and high accuracy. The effectiveness of this method is verified by simulation and physical experiments on a rotation SINS.