Abstract
The ability to update information acquired through various means online during task execution is crucial for a general-purpose service robot. This information includes geometric and semantic data. While SLAM handles geometric updates on 2D maps or 3D point clouds, online updates of semantic information remain unexplored. We attribute the challenge to the online scene graph representation, for its utility and scalability. Building on previous works regarding offline scene graph representations, we study online graph representations of semantic information in this work. We introduce SPARK: Spatial Perception and Robot Knowledge Integration. This framework extracts semantic information from environment-embedded cues and updates the scene graph accordingly, which is then used for subsequent task planning. We demonstrate that graph representations of spatial relationships enhance the robot system's ability to perform tasks in dynamic environments and adapt to unconventional spatial cues, like gestures.
Abstract (translated)
在线更新通过各种手段获取的信息对于通用服务机器人在任务执行过程中的信息处理至关重要，这些信息包括几何和语义数据。虽然SLAM（同步定位与地图构建）可以处理二维地图或三维点云的几何更新问题，但关于语义信息的在线更新仍然未被充分探索。我们把挑战归因于在线场景图表示的有效性和可扩展性。在前人对离线场景图表示工作的基础上，我们在本工作中研究了语义信息的在线图形表示方法。 为此，我们提出了SPARK框架：空间感知与机器人知识集成。该框架从环境中嵌入的线索中提取语义信息，并相应地更新场景图，然后将此用于后续的任务规划。我们证明，通过使用空间关系的图表示方式可以增强机器人的系统能力，在动态环境中的任务执行表现和适应非常规的空间提示（如手势）方面的能力得到提升。
URL
https://arxiv.org/abs/2506.20394
