原文：CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction

这是一篇CNN结合SLAM的经典论文，事实证明，以深度学习去替换SLAM中的一个/几个模块，是可行的，只要实验数据比纯几何SLAM要好，就是可以接受的。此篇文章使用CNN替换了SLAM中的数个模块，值得一看。原文链接我放在这儿：
链接：https://pan.baidu.com/s/1X91XTqIH8nV5yZ6smh1aYA
提取码：zm18
在我的下一篇博客里，也会贴上我自己翻译的译文，仅供参考。
Abstract
Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for accurate and dense monocular reconstruction. We propose a method where CNN-predicted dense depth maps are naturally fused together with depth measurements obtained from direct monocular SLAM. Our fusion scheme privileges depth prediction in image locations where monocular SLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa. We demonstrate the use of depth prediction for estimating the absolute scale of the reconstruction, hence overcoming one of the major limitations of monocular SLAM. Finally, we propose a framework to efficiently fuse semantic labels, obtained from a single frame, with dense SLAM, yielding semantically coherent scene reconstruction from a single view. Evaluation results on two benchmark datasets show the robustness and accuracy of our approach.