Drone localization is essential for various purposes such as navigation, autonomous flight, and object tracking. However, this task is challenging when satellite signals are unavailable. This paper addresses database-free vision-only localization of flying drones using optimal window template matching and velocity fusion. Assuming the ground is flat, multiple optimal windows are derived from a piecewise linear segment (regression) model of the image-to-real world conversion function. The optimal window is used as a fixed region template to estimate the instantaneous velocity of the drone. The multiple velocities obtained from multiple optimal windows are integrated by a hybrid fusion rule: a weighted average for lateral (sideways) velocities, and a winner-take-all decision for longitudinal velocities. In the experiments, a drone performed a total of six medium-range (800 m to 2 km round trip) and high-speed (up to 14 m/s) maneuvering flights in rural and urban areas. The flight maneuvers include forward-backward, zigzags, and banked turns. Performance was evaluated by root mean squared error (RMSE) and drift error of the GNSS-derived ground-truth trajectories and rigid-body rotated vision-only trajectories. Four fusion rules (simple average, weighted average, winner-take-all, hybrid fusion) were evaluated, and the hybrid fusion rule performed the best. The proposed video stream-based method has been shown to achieve flight errors ranging from a few meters to tens of meters, which corresponds to a few percent of the flight length.
Seokwon Yeom (Mon,) studied this question.