Scan matching in simultaneous localization and mapping (SLAM) has several technical issues, such as error accumulation, high processing cost in point cloud matching and optimization, and position loss problems after scan matching failures. Error adjustment processing can improve the performance of SLAM with loop closure and global optimization approach. However, measurement path plans and higher processing cost are required for the error adjustment and global optimization. In contrast, global navigation satellite system (GNSS) positioning can simplify the scan matching. Thus, we propose scan matching for multilayer LiDAR data registration with RTK-GNSS positioning and geometric constraints. Through experiments on point cloud acquisition with multilayer LiDAR and a single-frequency RTK-GNSS positioning device, we verify that our methodology can integrate point clouds acquired in mobile mapping without an inertial measurement unit. We also confirm that our methodology can avoid error accumulation problems in conventional SLAM processing.