Flapping-Wing Micro Air Vehicles (FWMAVs) operate under severe Size, Weight, and Power (SWaP) constraints, and their high-frequency flapping produces strong oscillatory dynamics that degrade conventional frame-based visual perception. Event cameras are well suited to these conditions, but event-based visual-inertial odometry (EVIO) methods typically rely on short time-window, locally linear motion assumptions that flapping flight breaks: under flapping, event-alignment methods such as plane fitting and contrast maximization recover mostly the high-frequency flapping-induced body rotation rather than the platform's translation. This work presents the first full EVIO pipeline running entirely on-board a small, high-frequency flapping-wing platform. The front-end deliberately avoids short-window alignment and instead exploits the abundance of flapping-induced events: it detects corner features event-by-event, clusters them into compact spatiotemporal features, and tracks these while discarding the fine oscillatory structure. The resulting feature tracks are fused with inertial measurements in a tightly coupled Extended Kalman Filter. The complete pipeline runs in real time on an on-board microcontroller within a sensing-and-compute payload of roughly 21 g. On flight datasets recorded on-board the FWMAV, the pipeline recovers velocity and attitude during flapping flight, and in a closed-loop flight test the on-board position estimate is accurate enough to hold a stationary hover in the horizontal plane. The same event stream that breaks conventional EVIO assumptions is thus sufficient not only for on-board state estimation, but for closing a position-control loop in flight.
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