让机器人实时更新环境语义信息，提升动态任务规划能力。
SPARK: Graph-Based Online Semantic Integration System for Robot Task Planning
- 基于图结构构建在线语义信息表示，支持动态更新。
- 通过空间关系图增强机器人对手势等非常规提示的响应能力。
- 适用于需要实时环境理解的服务机器人系统。
在任务执行过程中，对通过多种方式获取的信息进行在线更新，对通用服务机器人至关重要。这些信息包括几何与语义数据。尽管SLAM可处理2D地图或3D点云的几何更新，但语义信息的在线更新仍属空白。我们认为挑战源于在线场景图表示的复杂性，因其具备实用性与可扩展性。基于此前关于离线场景图表示的研究，本文研究了语义信息的在线图表示。提出SPARK：空间感知与机器人知识集成框架。该框架从环境嵌入线索中提取语义信息，并相应更新场景图，用于后续任务规划。实验表明，空间关系图能显著提升机器人在动态环境中的任务执行能力，并适应如手势等非标准空间提示。
原文摘要 · Abstract (English)
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.
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