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

Robust Incremental State Estimation through Covariance Adaptation