Recent work in the construction of 3D scene graphs has enabled mobile robots to build large-scale metric-semantic hierarchical representations of the world. These detailed models contain information that is useful for planning, however an open question is how to derive a planning domain from a 3D scene graph that enables efficient computation of executable plans. In this work, we present a novel approach for defining and solving Task and Motion Planning problems in large-scale environments using hierarchical 3D scene graphs. We describe a method for building sparse problem instances which enables scaling planning to large scenes, and we propose a technique for incrementally adding objects to that domain during planning time that minimizes computation on irrelevant elements of the scene graph. We evaluate our approach in two real scene graphs built from perception, including one constructed from the KITTI dataset. Furthermore, we demonstrate our approach in the real world, building our representation, planning in it, and executing those plans on a real robotic mobile manipulator. A video supplement is available at \url{https://youtu.be/v8fkwLjBn58}.

翻译：近期三维场景图的构建研究使得移动机器人能够建立大规模度量-语义层次化的世界表示。这些精细模型包含对规划有用的信息，然而一个悬而未决的问题是如何从三维场景图中推导出能够高效计算可执行规划的规划域。本工作提出了一种利用层次化三维场景图在大规模环境中定义和解决任务与运动规划问题的新方法。我们描述了一种构建稀疏问题实例的方法，使得规划能够扩展至大规模场景；并提出一种在规划过程中向该域增量添加对象的技术，以最小化对场景图中无关元素的计算开销。我们在两个基于感知构建的真实场景图中评估了所提方法，其中一个场景图基于KITTI数据集构建。此外，我们在真实世界中演示了该方法：构建场景表示、进行规划并在真实机器人移动操作平台上执行规划。视频补充材料可见于 \url{https://youtu.be/v8fkwLjBn58}。