A Unified Initial Alignment Method of SINS Based on FGO

TL;DR

This paper introduces a unified initial alignment method for SINS using factor graph optimization, improving heading accuracy by jointly estimating attitude, IMU bias, and misalignment over multiple time steps.

Contribution

It proposes a novel FGO-based approach that jointly estimates attitude, bias, and misalignment, enhancing initial alignment accuracy for SINS.

Findings

Improved heading accuracy in limited alignment time

Joint estimation reduces errors compared to traditional methods

Physical experiments validate the effectiveness of the proposed method

Abstract

The initial alignment provides an accurate attitude for SINS (strapdown inertial navigation system). 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 OBA (optimization-based alignment) uses MLE (maximum likelihood estimation) to find the optimal attitude quickly. However, few methods consider the IMU bias and misalignment angle, which will reduce the attitude accuracy. In this paper, a unified method based on FGO (Factor graph optimization) and IBF (inertial base frame) 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.