Efficient velocity estimation for MAVs by fusing motion from two frontally parallel cameras

Abstract Efficient velocity estimation is crucial for the robust operation of navigation control loops of micro aerial vehicles (MAVs). Motivated by the research on how animals exploit their visual topographies to rapidly perform locomotion, we propose a bio-inspired method that applies quasi-parallax technique to estimate the velocity of an MAV equipped with a forward-looking stereo camera without GPS. Different to the available optical flow-based methods, our method can realize efficient metric velocity estimation without applying any depth information from either additional distance sensors or from stereopsis. In particular, the quasi-parallax technique, which claims to press maximal benefits from the configuration of two frontally parallel cameras, leverages pairs of parallel visual rays to eliminate rotational flow for translational velocity estimation, followed by refinement of the estimation of rotational velocity and translational velocity iteratively and alternately. Our method fuses the motion information from two frontal-parallel cameras without performing correspondences matching, achieving enhanced robustness and efficiency. Extensive experiments on synthesized and actual scenes demonstrate the effectiveness and efficiency of our method.