论文标题
基于fgo的统一罪的初始对准方法
A Unified Initial Alignment Method of SINS Based on FGO
论文作者
论文摘要
最初的对齐方式为罪恶提供了准确的态度（横穿惯性导航系统）。通过进一步估计IMU的偏置和未对准角，递归贝叶斯过滤器是准确的。但是，先前的标题误差对收敛速度和准确性有重大影响。此外，精度将受到单个时间步长的迭代的限制。粗对齐方法OBA（基于优化的对准）使用MLE（最大似然估计）来快速找到最佳态度。但是，很少有方法考虑IMU偏置和未对准角度，这将降低态度的准确性。在本文中，提出了基于FGO（因子图优化）和IBF（惯性基础框架）的统一方法。通过MLE估计态度，IMU偏置和未对准角度通过MAP估计估算。所有时间步骤的状态都将共同优化，以进一步提高准确性。关于旋转MEMS罪的物理实验表明，该方法的标题准确性在有限的对齐时间内得到提高。
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.
