Because Lidar can directly obtain ranging information and is more robust than visual sensors to environmental changes such as illumination, the technology of laser synchronous location and mapping ( SLAM) has been widely developed in recent years. The traditional laser SLAM has made a lot of research achievements. But, it only uses geometric features, has limited understanding of the scene, and is difficult to deal with complex tasks. In addition, the current SLAM application scenarios have transited from traditional static scenes to complex dynamic scenes, and traditional methods are mostly difficult to achieve good performance due to interference of dynamic elements. Therefore, the 3D laser SLAM technology of semantic information enhancement has attracted more and more attention of researchers. The point cloud semantic tags are integrated with pure geometric features. On the one hand, the potential moving objects are filtered out with semantic information to solve the problem of static environmental assumptions. On the other hand, semantic information is used to assist the laser odometer to obtain high-precision positioning and mapping. This article summarizes the research progress of 3D laser SLAM technology for semantic information enhancement, puts forward a general framework for this technology, focuses on the outstanding research achievements and applications in this field in modules, and finally summarizes and prospects the development direction of this field.