Dynamic Covariance Estimation — A parameter free approach to robust Sensor Fusion

In robotics, non-linear least squares estimation is a common technique for simultaneous localization and mapping. One of the remaining challenges are measurement outliers leading to inconsistency or even divergence within the optimization process. Recently, several approaches for robust state estimation dealing with outliers inside the optimization back-end were presented, but all of them include at least one arbitrary tuning parameter that has to be set manually for each new application. Under changing environmental conditions, this can lead to poor convergence properties and erroneous estimates. To overcome this insufficiency, we propose a novel robust algorithm based on a parameter free probabilistic foundation called Dynamic Covariance Estimation. We derive our algorithm directly from the probabilistic formulation of a Gaussian maximum likelihood estimator. Through including its covariance in the optimization problem, we empower the optimizer to approximate these to the sensor's real properties. Finally, we prove the robustness of our approach on a real world wireless localization application where two similar state-of-the-art algorithms fail without extensive parameter tuning.