  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.
