To solve the problems of high density but uneven distribution of point cloud data collected by ummanned aerial vehicle (UAV) airborne lidar and incomplete information on the surface texture of insulators, a power grid insulator identification method based on airborne laser point cloud was proposed. Firstly, the histogram of the intensity value of different parts of the tower was analyzed, and the intensity value filter was used to remove most of the tower body point cloud; the principal component analysis method was then used to calculate the local point cloud eigenvalues. The local entropy function and spatial distribution characteristics based on the eigenvalues delete redundant flat area point clouds was built. Grid patching was used to avoid point cloud holes; finally, to solve the problem of low accuracy and slow speed of the traditional sample consensus initial alignment (SAC-IA) algorithm, the SAC-IA algorithm was improved to complete the pose estimation of the insulator by increasing the distance constraint relationship of the sampling point pair and adaptive adjustment parameters. The experimental results show that the insulators in the tower can be identified accurately and efficiently by using this method. And the running time is greatly reduced, and the extraction accuracy rate reaches 95.16%, which has good application value in UAV autonomous inspection route planning.