Traditional obstacle avoidance algorithm of Unmanned Aerial Vehicle (UAV) constructs the global map based onOctomap by visual sensor, which will produce large storage and is difficult to meet the engineering needs. The improvedalgorithmbased onThree-Dimensional Vector Histogram (3DVFH) only needs to construct a local map, but this method does notconsider previous data or operations, which can easily lead to unstable behavior and local minima. Regarding the above issues, anautonomous local obstacle avoidance algorithm based on enhanced 3DVFH is proposed, which directly replaces the global map witha 3D point cloud map provided by LiDAR, and a low-cost computational memory strategy to alleviate the inherent problems of localmethods is designed. Through experimental verification, it has effectively improved the efficiency and storage capacity ofenvironmental perception, which is conducive to better autonomous obstacle avoidance for UAV.