Semantic Layering in Room Segmentation via LLMs
This work addresses the problem of enhancing robotic navigation through more accurate and contextually relevant room segmentation, representing an incremental improvement by combining LLMs with existing segmentation methods.
The paper tackles the problem of semantic room segmentation in indoor environments by integrating Large Language Models (LLMs) with traditional 2D map-based segmentation to enrich maps with semantic data like object identification and spatial relationships, resulting in improved accuracy and contextual relevance as verified across 30 different 3D environments.
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.