VINS on wheels.

In this paper, we present a vision-aided inertial navigation system (VINS) for localizing wheeled robots. In particular, we prove that VINS has additional unobservable directions, such as the scale, when deployed on a ground vehicle that is constrained to move along straight lines or circular arcs. To address this limitation, we extend VINS to incorporate low-frequency wheel-encoder data, and show that the scale becomes observable. Furthermore, and in order to improve the localization accuracy, we introduce the manifold-(m)VINS that exploits the fact that the vehicle moves on an approximately planar surface. In our experiments, we first show the performance degradation of VINS due to special motions, and then demonstrate that by utilizing the additional sources of information, our system achieves significantly higher positioning accuracy, while operating in real-time on a commercial-grade mobile device.