论文标题
GraphMapper：有效的视觉导航逐场图生成
GraphMapper: Efficient Visual Navigation by Scene Graph Generation
论文作者
论文摘要
了解场景中对象之间的几何关系是使人类和自主代理在新环境中驾驭的核心能力。场景拓扑的稀疏，统一的表示将使代理商有效地采取行动以在其环境中移动，与他人进行环境状态，并利用代表来执行各种下游任务。为此，我们提出了一种培训自主代理的方法，通过同时学习在上述环境中导航，以学习积累其环境的3D场景图表。我们证明了我们的方法，GraphMapper，可以通过与基于视觉的系统更少的与环境的互动来学习有效的导航政策。此外，我们表明GraphMapper可以充当模块化场景编码器，与现有的基于学习的解决方案一起运行，不仅提高了导航效率，而且还会生成对其他未来任务有用的中间场景表示形式。
Understanding the geometric relationships between objects in a scene is a core capability in enabling both humans and autonomous agents to navigate in new environments. A sparse, unified representation of the scene topology will allow agents to act efficiently to move through their environment, communicate the environment state with others, and utilize the representation for diverse downstream tasks. To this end, we propose a method to train an autonomous agent to learn to accumulate a 3D scene graph representation of its environment by simultaneously learning to navigate through said environment. We demonstrate that our approach, GraphMapper, enables the learning of effective navigation policies through fewer interactions with the environment than vision-based systems alone. Further, we show that GraphMapper can act as a modular scene encoder to operate alongside existing Learning-based solutions to not only increase navigational efficiency but also generate intermediate scene representations that are useful for other future tasks.
