Robust and Efficient Velocity Estimation for MAVs With an RGB-D Camera

Micro Aerial Vehicles (MAVs) have become increasingly popular due to their wide applications in many areas. For autonomous navigation and safe control of MAVs, it is essential to have accurate and reliable velocity and position estimation. However, due to limited computational power and payload, it is still challenging for autonomous operation of MAVs in complex environments. In this paper, we propose a robust and efficient velocity estimation framework for MAVs with a single downward-facing RGB-D camera, which is able to provide metric velocity estimation in three dimensions as well as yaw rate in real-time without the fusion of additional sensors. Unlike traditional algorithms, our method uses kernel cross-correlators (KCCs) to efficiently determine optical flow for motion estimation, which does not rely on costly feature extraction and matching process. Moreover, we utilize depth images to estimate the vertical velocity of MAVs without the assumption of a flat ground. Autonomous flight tests on a quadrotor in complex environments demonstrate the robustness and efficiency of our method.