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.

通过将大型语言模型（LLMs）与传统的二维地图分割相结合，我们引入了语义分层（Semantic Layering）对于房间分割的先进方法（SeLRoS），丰富了分割地图的语义数据，包括物体识别和空间关系，以提高机器人导航的准确性和上下文相关性，并使用语义评估方法准确区分真实房间分隔和由家具和分割不准确性错误生成的房间分隔。

基于LLMs的房间分割中的语义分层