  The requirement to generate robust robotic platforms is a critical enabling
step to allow such platforms to permeate safety-critical applications (i.e.,
the localization of autonomous platforms in urban environments). One of the
primary components of such a robotic platform is the state estimation engine,
which enables the platform to reason about itself and the environment based
upon sensor readings. When such sensor readings are degraded traditional state
estimation approaches are known to breakdown. To overcome this issue, several
robust state estimation frameworks have been proposed. One such method is the
batch covariance estimation (BCE) framework. The BCE approach enables robust
state estimation by iteratively updating the measurement error uncertainty
model through the fitting of a Gaussian mixture model (GMM) to the measurement
residuals. This paper extends upon the BCE approach by arguing that the
uncertainty estimation process should be augmented to include metadata (e.g.,
the signal strength of the associated GNSS observation). The modification of
the uncertainty estimation process to an augmented data space is significant
because it increases the likelihood of a unique partitioning in the measurement
residual domain and thus provides the ability to more accurately characterize
the measurement uncertainty model. The proposed batch covariance estimation
over an augmented data-space (BCE-AD) is experimentally validated on collected
data where it is shown that a significant increase in state estimation accuracy
can be granted compared to previously proposed robust estimation techniques.
