论文标题
预测未开发环境中视觉导航的拓扑图
Predicting Topological Maps for Visual Navigation in Unexplored Environments
论文作者
论文摘要
我们为在未开发的环境中提出了一种用于自主探索和导航的机器人学习系统。我们的想法激发了我们的动机：即使是一个看不见的环境也可能从类似环境中的经验中熟悉。因此，我们方法的核心是用于构建，预测和使用概率布局图的过程，以帮助基于目标的视觉导航。我们比以前的艺术更快，准确地描述了使用布局预测来满足高级目标（例如“去厨房”）的导航系统。我们提出的导航框架包括三个阶段：（1）感知和映射：构建一个多级别3D场景图； （2）预测：预测未开发环境的概率3D场景图； （3）导航：辅助图形导航。我们在Matterport3D中测试我们的框架，并在看不见的环境中显示出更多的成功和有效的导航。
We propose a robotic learning system for autonomous exploration and navigation in unexplored environments. We are motivated by the idea that even an unseen environment may be familiar from previous experiences in similar environments. The core of our method, therefore, is a process for building, predicting, and using probabilistic layout graphs for assisting goal-based visual navigation. We describe a navigation system that uses the layout predictions to satisfy high-level goals (e.g. "go to the kitchen") more rapidly and accurately than the prior art. Our proposed navigation framework comprises three stages: (1) Perception and Mapping: building a multi-level 3D scene graph; (2) Prediction: predicting probabilistic 3D scene graph for the unexplored environment; (3) Navigation: assisting navigation with the graphs. We test our framework in Matterport3D and show more success and efficient navigation in unseen environments.
