通过测量误差协方差自适应实现鲁棒状态估计
Enabling Robust State Estimation through Measurement Error Covariance Adaptation
摘要：
精确的平台定位是大多数机器人系统的组成部分。随着这些机器人系统变得越来越普遍，有必要开发强大的状态估计算法，能够承受新的和非合作的环境。当处理新的和非合作的环境，很少知道先验的测量误差的不确定性，因此，有一个要求，定位算法的不确定性模型是自适应的。在本文中，我们提出了批量协方差估计技术，使强大的状态估计通过迭代适应的测量不确定性模型。通过残差的非参数聚类来实现测量不确定度模型的自适应，从而通过高斯混合模型来表征测量不确定度。所提供的高斯混合模型可以在任何非线性最小二乘优化算法内通过用所分配的聚类的充分统计来近似地表征每个观察（即，基于由非参数聚类算法提供的分配来更新每个观测的不确定性模型）。所提出的算法进行了验证，在几个GNSS收集的数据集，它表明，所提出的技术相比，其他鲁棒估计技术时，面对退化的数据质量时，表现出一定的优势。
英文摘要：
Accurate platform localization is an integral component of most robotic systems. As these robotic systems become more ubiquitous, it is necessary to develop robust state estimation algorithms that are able to withstand novel and non-cooperative environments. When dealing with novel and non-cooperative environments, little is known a priori about the measurement error uncertainty, thus, there is a requirement that the uncertainty models of the localization algorithm be adaptive. Within this paper, we propose the batch covariance estimation technique, which enables robust state estimation through the iterative adaptation of the measurement uncertainty model. The adaptation of the measurement uncertainty model is granted through non-parametric clustering of the residuals, which enables the characterization of the measurement uncertainty via a Gaussian mixture model. The provided Gaussian mixture model can be utilized within any non-linear least squares optimization algorithm by approximately characterizing each observation with the sufficient statistics of the assigned cluster (i.e., each observation's uncertainty model is updated based upon the assignment provided by the non-parametric clustering algorithm). The proposed algorithm is verified on several GNSS collected data sets, where it is shown that the proposed technique exhibits some advantages when compared to other robust estimation techniques when confronted with degraded data quality.
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