With the advent of unmanned aerial vehicles (UAVs), a major area of interest in the research field of UAVs has been vision-aided inertial navigation systems (V-INS). In the front-end of V-INS, image processing extracts information about the surrounding environment and determines features or points of interest. With the extracted vision data and inertial measurement unit (IMU) dead reckoning, the most widely used algorithm for estimating vehicle and feature states in the back-end of V-INS is an extended Kalman filter (EKF). An important assumption of the EKF is Gaussian white noise. In fact, measurement outliers that arise in various realistic conditions are often non-Gaussian. A lack of compensation for unknown noise parameters often leads to a serious impact on the reliability and robustness of these navigation systems. To compensate for uncertainties of the outliers, we require modified versions of the estimator or the incorporation of other techniques into the filter. The main purpose of this paper is to develop accurate and robust V-INS for UAVs, in particular, those for situations pertaining to such unknown outliers. Feature correspondence in image processing front-end rejects vision outliers, and then a statistic test in filtering back-end detects the remaining outliers of the vision data. For frequent outliers occurrence, variational approximation for Bayesian inference derives a way to compute the optimal noise precision matrices of the measurement outliers. The overall process of outlier removal and adaptation is referred to here as "outlier-adaptive filtering". Even though almost all approaches of V-INS remove outliers by some method, few researchers have treated outlier adaptation in V-INS in much detail. Here, results from flight datasets validate the improved accuracy of V-INS employing the proposed outlier-adaptive filtering framework.

中文翻译：

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用于视觉辅助惯性导航的鲁棒离群值自适应滤波。

随着无人飞行器（UAV）的出现，无人飞行器研究领域的一个主要兴趣领域是视觉辅助惯性导航系统（V-INS）。在V-INS的前端，图像处理提取有关周围环境的信息并确定特征或兴趣点。借助提取的视觉数据和惯性测量单元（IMU）航位推算，用于估计V-INS后端车辆和特征状态的最广泛使用的算法是扩展卡尔曼滤波器（EKF）。EKF的一个重要假设是高斯白噪声。实际上，在各种实际条件下出现的测量异常值通常都是非高斯的。缺乏对未知噪声参数的补偿通常会严重影响这些导航系统的可靠性和鲁棒性。为了补偿异常值的不确定性，我们需要修改估算器或将其他技术纳入过滤器。本文的主要目的是为无人机，特别是那些与此类未知异常情况有关的V-INS，开发准确而强大的V-INS。图像处理前端中的特征对应会拒绝视觉异常值，然后过滤后端的统计测试将检测视觉数据的其余异常值。对于频繁出现的异常值，贝叶斯推断的变分近似推导了一种计算测量异常值的最佳噪声精度矩阵的方法。离群值去除和适应的整个过程在此称为“离群值自适应过滤”。即使几乎所有的V-INS方法都可以通过某种方法消除异常值，很少有研究人员对V-INS中的离群值适应进行更详细的研究。在这里，来自飞行数据集的结果验证了采用提出的离群值自适应滤波框架的V-INS的改进精度。