Compared with traditional stereo vision,monocular vision has the advantages of simple equipment and low computational cost,which has been widely used in the field of visual obstacle avoidance.However,different monocular visual obstacle avoidance algorithms differ greatly in robustness,accuracy,real-time and other indicators,so trade-offs need to be made in practical use.Therefore,research progress and status of obstacle avoidance methods based on optical flow,feature matching and machine learning are analyzed.The proposed three methods are compared in terms of robustness,accuracy and real-time,and the advantages and disadvantages of each method are listed.Moreover,the challenges that currently constrain the development of monocular visual obstacle avoidance for UAVs are given.It is pointed out that the low cost,high precision and intelligent obstacle avoidance scheme are the future key research directions in the field of UAV monocular vision obstacle avoidance.
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