【摘要】 由于激光雷达可直接获得测距信息且相较于视觉传感器对光照等环境变化更具鲁棒性等优点，激光同步定位与建图(SLAM)技术近年来得到广泛发展。传统激光SLAM已取得很多研究成果，但其仅利用几何特征，对场景的理解有限，难以应对复杂任务，除此之外，当前SLAM应用场景已由传统静态场景向复杂动态场景过渡，传统方法由于动态元素干扰大多难以获得较好的性能。因此，语义信息增强的三维(3D)激光SLAM技术愈发受到研究学者们的关注，通过赋予点云语义标签与纯几何特征进行融合，一方面借助语义信息滤除潜在运动对象以解决静态环境假设问题，另一方面以语义信息辅助激光里程计获得高精度的定位与建图。综述了语义信息增强的3D激光SLAM技术研究进展，提出了该技术通用框架，分模块对该领域的突出研究成果及应用进行重点介绍，最后对该领域发展方向进行了总结与展望。更多还原

【Abstract】 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.更多还原