  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.
