Towards robust graphical models for GNSS-based localization in urban environments

Our paper proposes to adapt recent advances in the SLAM literature (Simultaneous Localization and Mapping) to the problem of multipath mitigation in GNSS-based localization. We argue that such localization problems can be modelled as factor graphs and solved using efficient nonlinear least squares approaches that exploit the sparsity inherent in the problem formulation. This way, satellite observations that are subject to multipath errors can be understood as outliers in the optimization problem and recently developed approaches for robust graph optimization can be applied to mitigate these effects.