张宁  GEN-SLAM - Generative Modeling for Monocular Simultaneous Localization and Mapping
https://arxiv.org/abs/1902.02086

Punarjay Chakravarty, Praveen Narayanan and Tom Roussel

Abstract—We present a Deep Learning based system for the twin tasks of localization and obstacle avoidance essential to any mobile robot. Our system learns from conventional geometric SLAM, and outputs, using a single camera, the topological pose of the camera in an environment, and the depth map of obstacles around it. We use a CNN to localize in a topological map, and a conditional VAE to output depth for a camera image, conditional on this topological location estimation. We demonstrate the effectiveness of our monocular localization and depth estimation system on simulated and real datasets.

我们提出了一个基于深度学习的系统，用于任何移动机器人必不可少的定位和避障的双重任务。我们的系统从传统的几何SLAM中学习，并使用单个摄像机输出摄像机在环境中的拓扑姿势，以及周围障碍物的深度图。我们使用CNN来定位拓扑图，并使用条件VAE来输出相机图像的深度，条件是这个拓扑位置估计。我们证明了我们的单目定位和深度估计系统对模拟和真实数据集的有效性。