To address the issue of drift errors and inadequate precision in point clouds produced by laser-based simultaneous localization and mapping (SLAM) algorithms during lengthy scanning trajectories, this study presents a global point cloud registration approach for laser SLAM that relies on graph optimization. We constructed initial and iterative pose graphs for cascaded optimization in succession for laser SLAM point clouds with specific drift errors. The pose graph is initially created using point cloud similarity and centroid distance of segments to reduce trajectory drift error, resulting in SLAM point clouds with smaller drift errors. From this, iterative pose graphs are formed based on the overlap of point clouds between segments. Subsequently, the point clouds are coarsely and finely adjusted in an iterative manner to produce higher precision SLAM point clouds. Experiments were performed in this paper using one set of handheld and three sets of vehicle-mounted laser SLAM data. After optimization, the point clouds of the four experimental data sets were well overlapped by their respective repeated scans. The distance root mean square error (RMSE) between the matched keypoints is reduced to 0.158, 0.211, 0.218, and 0.157 m from 2.667, 10.348, 19.018, and 3.412 m, respectively, before the optimization. Experimental results indicate that the proposed algorithm can resolve the issue of drift error during laser SLAM point cloud long trajectory scanning, ultimately improving the accuracy of the point cloud data.