  Non-Gaussian and multimodal distributions are an important part of many
recent robust sensor fusion algorithms. In difference to robust cost functions,
they are probabilistically founded and have good convergence properties. Since
their robustness depends on a close approximation of the real error
distribution, their parametrization is crucial. We propose a novel approach
that allows to adapt a multi-modal Gaussian mixture model to the error
distribution of a sensor fusion problem. By combining expectation-maximization
and non-linear least squares optimization, we are able to provide a
computationally efficient solution with well-behaved convergence properties. We
demonstrate the performance of these algorithms on several real-world GNSS and
indoor localization datasets. The proposed adaptive mixture algorithm
outperforms state-of-the-art approaches with static parametrization. Source
code and datasets are available under https://mytuc.org/libRSF.
