The initial alignment process can provide an accurate initial attitude of
strapdown inertial navigation system. The conventional two-procedure method
usually includes coarse and fine alignment processes. Coarse alignment
converges fast because of its batch estimating characteristics and the initial
attitude does not influence the results. But coarse alignment is low accuracy
without considering the IMU's bias. The fine alignment is more accurate by
applying a recursive Bayesian filter to estimate the IMU's bias, but the
attitude converges slowly as the initial value influence the convergence speed
of the recursive filter. Researchers have proposed the unified initial
alignment to achieve initial alignment in one procedure, existing unified
methods make improvements on the basics of recursive Bayesian filter and those
methods are still slow to converge. In this paper, a unified method based on
batch estimator FGO (factor graph optimization) is raised, which is converge
fast like coarse alignment and accurate than the existing method. We redefine
the state and rederivation the state dynamic model first. Then, the optimal
attitude and the IMU's bias are estimated simultaneously through FGO. The fast
convergence and high accuracy of this method are verified by simulation and
physical experiments on a rotation SINS.Comment: 9 pages, Journal Paper