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

本研究探讨了语义映射中几何与知识表示的整合不足的问题。通过采用深度学习技术，该论文提出了将知识图谱和语言概念完全融合到传感器数据处理及语义映射管道中的新方法。研究表明，这种在线知识整合方法显著推动了语义映射的发展，开启了以前无法实现的新应用。

在线知识整合用于3D语义映射：综述