This paper proposes a laser simultaneous localization and mapping (SLAM) method to further enhance robot positioning and mapping capabilities in complex environments by improving the algorithm stability and robustness. The proposed method is based on a factor graph optimization framework that optimizes the front-end feature point extraction method to identify corner, plane, and surface points. Point curve constraint equations are introduced during local map matching. In the back-end, radar odometry and inertial measurement unit pre-integration factors are added to the factor graph to optimize the global pose. The experimental results obtained on the KITTI dataset and a self-collected dataset demonstrate that, compared to lidar-inertial odometry with SLAM (LIO-SAM), the proposed method achieves an approximately 15% improvement in the absolute trajectory error improvement, a 7% increase in the number of point clouds in the feature point neighborhood, and a substantial increase in the number of feature points. These improvements effectively enhance the performance of the proposed SLAM system compared to LIO-SAM.