In this paper, we introduce Semantic Layering in Room Segmentation via LLMs (SeLRoS), an advanced method for semantic room segmentation by integrating Large Language Models (LLMs) with traditional 2D map-based segmentation. Unlike previous approaches that solely focus on the geometric segmentation of indoor environments, our work enriches segmented maps with semantic data, including object identification and spatial relationships, to enhance robotic navigation. By leveraging LLMs, we provide a novel framework that interprets and organizes complex information about each segmented area, thereby improving the accuracy and contextual relevance of room segmentation. Furthermore, SeLRoS overcomes the limitations of existing algorithms by using a semantic evaluation method to accurately distinguish true room divisions from those erroneously generated by furniture and segmentation inaccuracies. The effectiveness of SeLRoS is verified through its application across 30 different 3D environments. Source code and experiment videos for this work are available at: https://sites.google.com/view/selros.

翻译：本文提出基于大语言模型的房间分割语义层次方法（SeLRoS），这是一种将大语言模型（LLMs）与传统二维地图分割相结合的高级语义房间分割技术。不同于以往仅关注室内环境几何分割的方法，本研究通过集成物体识别与空间关系等语义数据来丰富分割地图，从而增强机器人导航能力。我们利用LLMs构建了一个新颖框架，该框架能够解释并组织每个分割区域内的复杂信息，进而提升房间分割的准确性与上下文相关性。此外，SeLRoS通过采用语义评估方法，准确区分真实房间划分与因家具及分割误差产生的伪划分，克服了现有算法的局限性。在30个不同三维环境中的实验验证了SeLRoS的有效性。本工作的源代码与实验视频详见：https://sites.google.com/view/selros。