Aiming to address the difficulties in the automatic registration of non-homologous laser scanning and photogrammetric point cloud data, a method based on fast point feature histogram (FPFH) point cloud coarse registration and the octree grid iteration improved nearest neighbor (ICP) algorithm is proposed. For coarse registration, a voxel grid is used to desample the point cloud data before applying the FPFH for feature matching. Finally, the sampling consistent initial registration (SAC-IA) algorithm is used to obtain the initial registration transformation matrix. For fine registration, the classical ICP algorithm is used to eliminate the wrong corresponding points by setting the Euclidean distance threshold. Then, the homonymous point pairs with the highest accuracy are selected in each voxel grid, and the final registration transformation matrix is calculated using the singular value decomposition (SVD) method. The experimental results show that the proposed method can be used to solve the registration problem of non-homologous data between laser scanning and photogrammetric point clouds and has certain research and application value.