Semantic Layering in Room Segmentation via LLMs (2403.12920v1)

Abstract: In this paper, we introduce Semantic Layering in Room Segmentation via LLMs (SeLRoS), an advanced method for semantic room segmentation by integrating 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.

• The paper introduces SeLRoS, a method combining geometric segmentation with LLM-based semantic integration to enhance indoor map accuracy.
• The methodology fuses Voronoi Random Field segmentation, object detection, and prompt engineering to layer semantic data onto 2D maps.
• Results show significant improvements in segmentation accuracy, validated through IoU and the new MSIoU metric across 30 diverse 3D environments.

Semantic Layering in Room Segmentation via LLMs: An Analytical Overview

This paper introduces a novel approach to room segmentation, designated as Semantic Layering in Room Segmentation via LLMs (SeLRoS). It proposes the integration of LLMs with traditional 2D map-based segmentation methods to enhance the semantic richness of segmented maps within indoor environments, thus facilitating improved robotic navigation.

The primary innovation of SeLRoS lies in its ability to meld semantic data, which includes object identification and spatial relationships, into pre-existing geometric segmentation frameworks. Unlike conventional segmentation techniques that focus predominantly on geometric boundaries, SeLRoS enriches the understanding of each room by incorporating semantic layers. The methodology highlights a novel framework that interprets and organizes complex spatial information, laying a foundation for both more accurate segmentation and more contextually relevant navigation.

Key among the contributions of this paper is the semantic evaluation mechanism, a notable departure from traditional segmentation algorithms that often misinterpret room divisions due to furniture and other inanimate objects. Through this mechanism, SeLRoS effectively disambiguates distinct rooms from erroneously segmented spaces, a capability verified through exhaustive testing across 30 diverse 3D environments. Results demonstrate that SeLRoS significantly advances segmentation maps' accuracy and utility.

SeLRoS is structured into three core processes: geometric room segmentation, object mapping, and semantic integration. The geometric segmentation employs Voronoi Random Field (VRF) methods to delineate spatial boundaries within 2D maps, providing a preliminary segmentation map. Object mapping utilizes advanced object detection algorithms to identify and map the presence of objects within each segmented space, creating a matrix of object-based data for subsequent semantic layering. The culmination of the process—semantic integration—deploys prompt engineering to transform collected data into structured inputs for LLMs, which in turn produce enriched semantic information.

The experimental framework validates the efficacy of SeLRoS through both qualitative and quantitative analysis. The paper reports robust improvements in segmentation accuracy compared to existing methodologies, substantiated through Intersection over Union (IoU) and a newly proposed evaluation metric, Match Scaled Intersection over Union (MSIoU). This new metric refines conventional accuracy assessments by incorporating room correspondence quality into segmentation evaluation.

The implications of this research are substantial. On a theoretical front, SeLRoS challenges the traditional segmentation paradigm by positing semantic integration as an indispensable component for context-aware mapping solutions. Practically, the enriched segmentation maps bear significant promise for advancing autonomous navigation capabilities in robotics, enabling more precise and intuitive interaction with complex indoor environments.

The semantic integration facilitated by LLMs also marks a forward step in utilizing AI for enhanced interpretative capabilities in unmapped domains—an approach that may yield further improvements in a variety of fields such as augmented reality and intelligent building management systems. While the paper acknowledges certain limitations, including the potential for misclassifications and the need for refined object relation criteria, these present opportunities for further research and refinement.

In conclusion, SeLRoS demonstrates an effective synergy between geometric segmentation and semantic enhancement via LLMs, paving the way towards more sophisticated and semantically enriched room segmentation methodologies. Future work will likely extend SeLRoS by addressing its limitations, potentially integrating more advanced machine learning techniques and exploring cross-domain applications.