Hybrid visual and inertial RANSAC for real-time motion estimation

We present a real-time, online motion estimation algorithm combining inertial and visual measurements. The approach is built upon the preemptive RANSAC, a real-time motion estimation algorithm. Our algorithm extends the preemptive RANSAC with inertial sensors data, introducing a lagrangian hybrid scoring of the motion models. We also improve the system using a pure inertial model that is scored as the visual ones, and a dynamic computation of the lagrangian to make the approach adaptive to various image and motion contents. The algorithm is run frame to frame to avoid error accumulation. All these improvements are made with little computational cost, keeping the complexity of the algorithm low enough for embedded platforms. The approach is compared with pure inertial and pure visual procedures.