In order to improve the accuracy and efficiency of existing registration algorithms, a point cloud registration algorithm based on point cloud feature vector extraction was proposed. The point curvature and the number of points in the neighborhood are used as comprehensive criterion to filter feature points, and then feature vectors are extracted by principal component analysis of feature points. The transformation relationship of feature vectors is used to solve the transformation matrix between the point clouds to achieve rough registration of point clouds. In the precise registration, the point cloud k-dimensional binary tree is created, and the nearest neighbor search by the k-dimensional binary tree is used to improve the precision registration efficiency of the ICP algorithm. The proposed algorithm was compared with a variety of algorithms in the public data sets Bunny and Horse and the measured environmental point cloud data to verify the effectiveness. The results show that computation time is reduced by 60% compared with ICP algorithm, and the proposed algorithm has good accuracy and registration efficiency.