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。
Accurate and robust pose estimation is a fundamental capability for autonomous systems to navigate, map and perform tasks. Particularly, construction environments pose challenging problem to Simultaneous Localization and Mapping (SLAM) algorithms due to sparsity, varying illumination conditions, and dynamic objects. Current academic research in SLAM is focused on developing more accurate and robust algorithms for example by fusing different sensor modalities. To help this research, we propose a new dataset, the Hilti SLAM Challenge Dataset. The sensor platform used to collect this dataset contains a number of visual, lidar and inertial sensors which have all been rigorously calibrated. All data is temporally aligned to support precise multi-sensor fusion. Each dataset includes accurate ground truth to allow direct testing of SLAM results. Raw data as well as intrinsic and extrinsic sensor calibration data from twelve datasets in various environments is provided. Each environment represents common scenarios found in building construction sites in various stages of completion.