A Look at Improving Robustness in Visual-inertial SLAM by Moment Matching
This work addresses robustness issues in ego-motion tracking for autonomous and smart devices, representing an incremental improvement over existing methods.
The paper tackled the problem of improving robustness in visual-inertial SLAM by addressing limitations of the extended Kalman filter under faulty visual features and noise, using a moment matching approach to achieve state-of-the-art results on the EuRoC MAV drone benchmark.
The fusion of camera sensor and inertial data is a leading method for ego-motion tracking in autonomous and smart devices. State estimation techniques that rely on non-linear filtering are a strong paradigm for solving the associated information fusion task. The de facto inference method in this space is the celebrated extended Kalman filter (EKF), which relies on first-order linearizations of both the dynamical and measurement model. This paper takes a critical look at the practical implications and limitations posed by the EKF, especially under faulty visual feature associations and the presence of strong confounding noise. As an alternative, we revisit the assumed density formulation of Bayesian filtering and employ a moment matching (unscented Kalman filtering) approach to both visual-inertial odometry and visual SLAM. Our results highlight important aspects in robustness both in dynamics propagation and visual measurement updates, and we show state-of-the-art results on EuRoC MAV drone data benchmark.