In recent years, LiDAR-based localization has been widely explored. Among them, Scene Coordinate Regression (SCR)-based methods have demonstrated outstanding accuracy and robustness in city scenes. Integrating these models with traditional Simultaneous Localization and Mapping (SLAM) methods is expected to enhance localization accuracy and reliability further. This paper proposes loosely coupled fusion methods integrating an SCR model with SLAM to improve localization accuracy and robustness. The approach addresses the information loss problem in high-level sensor fusion while maintaining computational efficiency. The method achieves tighter data association and complementary performance advantages by strategically combining LiDAR-based localization results with SLAM pose estimates. Experimental results in the NCLT and HeLiPR datasets demonstrate that the proposed fusion framework effectively corrects SLAM drift and maintains stable pose estimation accuracy under diverse environmental conditions. Furthermore, the sparse-frame coupling strategy significantly reduces computational overhead without degrading localization performance, making the method suitable for practical applications. The system exhibits improved robustness across regions and LiDAR configurations while preserving real-time operation capabilities.