  Underwater visual-inertial navigation is challenging due to the poor
visibility and presence of outliers in underwater environments. The navigation
performance is closely related to outlier detection and elimination. Existing
methods assume the inertial odometry is accurate enough for outlier detection,
which is not valid for low-cost inertial applications. We propose a novel
iterative smoothing and outlier detection method aiming for underwater
navigation. Using the dataset collected from an underwater robot and fiducial
markers, experimental results confirm that the method can successfully
eliminate the outliers and enhance navigation accuracy.
