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The fusion of camera sensor and inertial data is a leading method forego-motion tracking in autonomous and smart devices. State estimationtechniques that rely on non-linear filtering are a strong paradigm for solvingthe associated information fusion task. The de facto inference method in thisspace is the celebrated extended Kalman filter (EKF), which relies onfirst-order linearizations of both the dynamical and measurement model. Thispaper takes a critical look at the practical implications and limitations posedby the EKF, especially under faulty visual feature associations and thepresence of strong confounding noise. As an alternative, we revisit the assumeddensity formulation of Bayesian filtering and employ a moment matching(unscented Kalman filtering) approach to both visual-inertial odometry andvisual SLAM. Our results highlight important aspects in robustness both indynamics propagation and visual measurement updates, and we showstate-of-the-art results on EuRoC MAV drone data benchmark.

A Look at Improving Robustness in Visual-inertial SLAM by Moment Matching