论文标题
视觉导航的神经拓扑大满贯
Neural Topological SLAM for Visual Navigation
论文作者
论文摘要
本文研究了图像目标导航的问题，该问题涉及导航到以前看不见的环境中的目标图像指示的位置。为了解决这个问题，我们为空间设计拓扑表示，这些拓扑表示有效地利用语义并提供了近似的几何推理。我们表示的核心是具有相关语义特征的节点，这些节点是使用粗糙几何信息互连的。我们描述了可以在嘈杂的驱动下建立，维护和使用此类表示的基于学习的算法。在视觉和物理上逼真的模拟中进行的实验研究表明，我们的方法建立了有效的表示，以捕获结构规律性并有效地解决长距离导航问题。我们观察到比研究此任务的现有方法的相对改善超过50％。
This paper studies the problem of image-goal navigation which involves navigating to the location indicated by a goal image in a novel previously unseen environment. To tackle this problem, we design topological representations for space that effectively leverage semantics and afford approximate geometric reasoning. At the heart of our representations are nodes with associated semantic features, that are interconnected using coarse geometric information. We describe supervised learning-based algorithms that can build, maintain and use such representations under noisy actuation. Experimental study in visually and physically realistic simulation suggests that our method builds effective representations that capture structural regularities and efficiently solve long-horizon navigation problems. We observe a relative improvement of more than 50% over existing methods that study this task.
