Visual-inertial navigation methods have been shown to be an effective, low-cost way to operate autonomously without GPS or other global measurements, however most filtering approaches to VI suffer from observability and consistency problems. To increase robustness of the state-of-the-art methods, we propose a three-fold improvement. First, we propose the addition of a linear drag term in the velocity dynamics which improves estimation accuracy. Second, we propose the use of a partial-update formulation which limits the effect of linearization errors in partially-observable states, such as sensor biases. Finally, we propose the use of a keyframe reset step to enforce observability and consistency of the normally unobservable position and heading states. While all of these concepts have been used independently in the past, our experiments demonstrate additional strength when they are used simultaneously in a visual-inertial state estimation problem. In this paper, we derive the proposed filter and use a Monte Carlo simulation experiment to analyze the response of visual-inertial Kalman filters with the above described additions. The results of this study show that the combination of all of these features significantly improves estimation accuracy and consistency.