Autonomous obstacle avoidance for UAV based on fusion of radar and monocular camera

UAVs face many challenges in autonomous obstacle avoidance in large outdoor scenarios, specifically the long communication distance from ground stations. The computing power of onboard computers is limited, and the unknown obstacles cannot be accurately detected. In this paper, an autonomous obstacle avoidance scheme based on the fusion of millimeter wave radar and monocular camera is proposed. The visual detection is designed to detect unknown obstacles which is more robust than traditional algorithms. Then extended Kalman filter (EKF) data fusion is used to build exact real 3D coordinates of the obstacles. Finally, an efficient path planning algorithm is used to obtain the path to avoid obstacles. Based on the theoretical design, an experimental platform is built to verify the UAV autonomous obstacle avoidance scheme proposed in this paper. The experiment results show the proposed scheme cannot only detect different kinds of unknown obstacles, but can also take up very little computing resources to run on an onboard computer. The outdoor flight experiment shows the feasibility of the proposed scheme.