近年来，深度学习在更高层级的视觉任务中取得瞩目的成绩，如:物体识别,语义分割等。这些课题曾是传统视觉无法或很难解决的任务。深度学习方法的这种能力拓展了我们对视觉任务的想象空间，越来越多的 SLAM 开始在他们的框架中通过融合学习的方法来改进位姿估计的准确程度和环境重建的效果。但是深度学习是一个非常宽广的领域,和 SLAM 相关的课题只是它的一个分支，本书稿将会挑选、聚焦与 SLAM 相关的深度学习任务，希望能通过这本书稿来介绍SLAM 系统中使用的几何和深度学习的方法，帮助读者掌握最新的进展。
LiDAR-based SLAM system is admittedly more accurate and stable than others, while its loop closure detection is still an open issue. With the development of 3D semantic segmentation for point cloud, semantic information can be obtained conveniently and steadily, essential for high-level intelligence and conductive to SLAM. In this paper, we present a novel semantic-aided LiDAR SLAM with loop closure based on LOAM, named SA-LOAM, which leverages semantics in odometry as well as loop closure detection. Specifically, we propose a semantic-assisted ICP, including semantically matching, downsampling and plane constraint, and integrates a semantic graph-based place recognition method in our loop closure detection module. Benefitting from semantics, we can improve the localization accuracy, detect loop closures effectively, and construct a global consistent semantic map even in large-scale scenes. Extensive experiments on KITTI and Ford Campus dataset show that our system significantly improves baseline performance, has generalization ability to unseen data and achieves competitive results compared with state-of-the-art methods.