Semantic Layering in Room Segmentation via LLMs

Описание: This is a supplementary video for the paper, titled "Semantic Layering in Room Segmentation via LLMs", by Taehyeon Kim and Byung-Cheol Min. It will be presented at 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Abu Dhabi, UAE, October 13-17, 2024.

Paper (ArXiv) Link: https://arxiv.org/abs/2403.12920

In this research, 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 can be found at: https://sites.google.com/view/selros.