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.
翻译:我们建议建立一个机器人学习系统,用于在未探索的环境中进行自主探索和导航。我们之所以提出这种想法,是因为即使一个看不见的环境也可能从类似环境中的以往经历中熟悉。因此,我们方法的核心是建立、预测和使用概率布局图以协助基于目标的视觉导航。我们描述一个使用布局预测实现高层次目标(例如“去厨房”)的导航系统,比以往更迅速、更准确。我们提议的导航框架包括三个阶段:(1) 概念和绘图:建立一个多层 3D 场景图;(2) 预测:预测未探索环境的3D 场景图;(3) 导航:协助图形导航。我们在Metport3D测试我们的框架,显示在看不见环境中更成功、更高效的导航。