基于变分贝叶斯的鲁棒自适应因子图优化组合导航算法

复杂环境下的量测粗差和时变噪声严重影响了状态估计的精度和可靠性，对此提出了一种基于变分贝叶斯的鲁棒自适应因子图优化组合导航算法。首先，基于先验和后验两阶段更新将变分贝叶斯推断引入因子图优化框架中，以估计时变量测噪声协方差；其次，利用相邻帧间的平均新息构造量测协方差预测值，作为粗差判据来实现稳健估计。基于INS/GNSS组合导航的仿真和现场实验评估表明，所提方法能在粗差干扰的情况下有效估计时变量测噪声，相比M估计和滑动窗口自适应因子图优化算法的水平定位误差分别减小了26.7%和39.8%,兼顾了估计精度和抗差性能，具有较好的复杂环境适应性。

Robust adaptive factor graph optimization integrated navigation algorithm based on variational Bayesian

The accuracy and reliability of state estimation are seriously affected by measurement outliers and time-varying noise in complex environments. To address these issues, a robust adaptive factor graph optimization (FGO) integrated navigation algorithm based on variational Bayesian is proposed. First, the variational Bayesian inference is introduced into the FGO framework based on a priori and a posteriori two-stage updating to estimate the time-varying measurement noise covariance. Secondly, the mean innovation between neighboring keyframes is used to construct measurement covariance prediction as an outlier judgment to achieve robust estimation. Simulation and field tests based on INS / GNSS integrated navigation show that the proposed method can effectively estimate the time-varying measurement noise covariance in the presence of outlier interference, and reduce the horizontal position error by 26. 7% and 39. 8% compared to the M-estimation and sliding window adaptive FGO algorithms, which takes into account the accuracy and robust performance. It has an excellent adaptation to complex scenarios.