  Recent advances in the fields of robotics and automation have spurred
significant interest in robust state estimation. To enable robust state
estimation, several methodologies have been proposed. One such technique, which
has shown promising performance, is the concept of iteratively estimating a
Gaussian Mixture Model (GMM), based upon the state estimation residuals, to
characterize the measurement uncertainty model. Through this iterative process,
the measurement uncertainty model is more accurately characterized, which
enables robust state estimation through the appropriate de-weighting of
erroneous observations. This approach, however, has traditionally required a
batch estimation framework to enable the estimation of the measurement
uncertainty model, which is not advantageous to robotic applications. In this
paper, we propose an efficient, incremental extension to the measurement
uncertainty model estimation paradigm. The incremental covariance estimation
(ICE) approach, as detailed within this paper, is evaluated on several
collected data sets, where it is shown to provide a significant increase in
localization accuracy when compared to other state-of-the-art robust,
incremental estimation algorithms.
