借助大语言模型为室内场景构建分层3D场景图以实现智能空间感知
Intelligent Spatial Perception by Building Hierarchical 3D Scene Graphs for Indoor Scenarios with the Help of LLMs
AI总结：
针对智能机器人导航对空间环境全面理解的需求，提出利用大语言模型构建含多层级节点的分层3D场景图的系统，通过LLM投票机制提升房间标注准确性，融合语义与几何数据生成精准环境表征，助力情境感知导航与任务规划。
摘要：
本文针对先进智能机器人导航对空间环境更全面理解的高需求，提出了一种利用大语言模型（LLMs）能力为室内场景构建分层3D场景图（3DSGs）的新型系统。所提出的框架构建的3DSGs包含：具有丰富度量-语义信息的基础层，以物体节点的精确点云表示及视觉描述符为特征的物体层，以及房间、楼层、建筑节点等更高层级。得益于LLMs的创新性应用，不仅物体节点，更高层级的节点（例如房间节点）都能得到智能且准确的标注。本文提出了一种利用LLMs进行房间分类的投票机制，以提升房间节点标注的准确性和可靠性。全面的数值实验证明，该系统能够将语义描述与几何数据相融合，生成准确且全面的环境表征，对情境感知导航和任务规划具有重要作用。
英文摘要：
This paper addresses the high demand in advanced intelligent robot navigation for a more holistic understanding of spatial environments, by introducing a novel system that harnesses the capabilities of Large Language Models (LLMs) to construct hierarchical 3D Scene Graphs (3DSGs) for indoor scenarios. The proposed framework constructs 3DSGs consisting of a fundamental layer with rich metric-semantic information, an object layer featuring precise point-cloud representation of object nodes as well as visual descriptors, and higher layers of room, floor, and building nodes. Thanks to the innovative application of LLMs, not only object nodes but also nodes of higher layers, e.g., room nodes, are annotated in an intelligent and accurate manner. A polling mechanism for room classification using LLMs is proposed to enhance the accuracy and reliability of the room node annotation. Thorough numerical experiments demonstrate the system's ability to integrate semantic descriptions with geometric data, creating an accurate and comprehensive representation of the environment instrumental for context-aware navigation and task planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
