将知识图谱与语言模型融入3D语义地图，实现动态知识融合。
Online Knowledge Integration for 3D Semantic Mapping: A Survey
- 通过语义场景图与语言模型实现符号化知识在线注入。
- 支持在建图过程中实时融合常识与自然语言概念。
- 适合研究机器人环境理解与智能交互的学者参考。
语义映射是机器人在结构化环境中操作与交互的关键组件。传统方法中，几何信息与知识表示在语义地图中仅松散结合。近年来，深度学习的发展使得先验知识（如知识图谱或语言概念）可完全融入传感器数据处理与语义映射流程。语义场景图与语言模型使现代方法能在建图过程及之后阶段，整合基于图的先验知识或利用人类语言中的丰富信息。这推动了语义映射的重大进展，催生出前所未有的新应用。本综述全面回顾了这些最新发展，重点关注知识在语义映射中的在线集成。特别聚焦于使用语义场景图整合符号先验知识，以及使用语言模型捕捉隐含常识与自然语言概念的方法。
原文摘要 · Abstract (English)
Semantic mapping is a key component of robots operating in and interacting with objects in structured environments. Traditionally, geometric and knowledge representations within a semantic map have only been loosely integrated. However, recent advances in deep learning now allow full integration of prior knowledge, represented as knowledge graphs or language concepts, into sensor data processing and semantic mapping pipelines. Semantic scene graphs and language models enable modern semantic mapping approaches to incorporate graph-based prior knowledge or to leverage the rich information in human language both during and after the mapping process. This has sparked substantial advances in semantic mapping, leading to previously impossible novel applications. This survey reviews these recent developments comprehensively, with a focus on online integration of knowledge into semantic mapping. We specifically focus on methods using semantic scene graphs for integrating symbolic prior knowledge and language models for respective capture of implicit common-sense knowledge and natural language concepts
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