语言扩展室内SLAM（LEXIS）：一种用于实时视觉场景理解的多功能系统
Language-EXtended Indoor SLAM (LEXIS): A Versatile System for Real-time Visual Scene Understanding
AI总结：
本文提出实时室内SLAM系统LEXIS，通过结合LLM开放词汇能力、CLIP特征与拓扑SLAM图，实现灵活房间理解、语义地点识别及回环优化，性能达到或超过SOTA。
摘要：
多功能且自适应的语义理解将使自主系统能够理解周围环境并与之交互。现有固定类别模型限制了室内移动和辅助自主系统的适应性。在本工作中，我们提出LEXIS，这是一种实时室内同步定位与地图构建（SLAM）系统，利用大语言模型（LLM）的开放词汇特性，创建场景理解和地点识别的统一方法。该方法首先（使用视觉惯性里程计）构建环境的拓扑SLAM图，并将对比语言-图像预训练（CLIP）特征嵌入图节点中。我们使用这种表示进行灵活的房间分类和分割，作为以房间为中心的地点识别的基础。这使得回环检测搜索能够被导向语义相关的地点。我们所提出的系统使用公开的仿真数据和真实世界数据进行评估，涵盖办公室和家庭环境。它成功地对布局和尺寸各异的房间进行分类，并优于最先进（SOTA）方法。在地点识别和轨迹估计任务上，我们取得了与SOTA相当的性能，所有这些也都使用相同的预训练模型。最后，我们展示了该系统在规划方面的潜力。
英文摘要：
Versatile and adaptive semantic understanding would enable autonomous systems to comprehend and interact with their surroundings. Existing fixed-class models limit the adaptability of indoor mobile and assistive autonomous systems. In this work, we introduce LEXIS, a real-time indoor Simultaneous Localization and Mapping (SLAM) system that harnesses the open-vocabulary nature of Large Language Models (LLMs) to create a unified approach to scene understanding and place recognition. The approach first builds a topological SLAM graph of the environment (using visual-inertial odometry) and embeds Contrastive Language-Image Pretraining (CLIP) features in the graph nodes. We use this representation for flexible room classification and segmentation, serving as a basis for room-centric place recognition. This allows loop closure searches to be directed towards semantically relevant places. Our proposed system is evaluated using both public, simulated data and real-world data, covering office and home environments. It successfully categorizes rooms with varying layouts and dimensions and outperforms the state-of-the-art (SOTA). For place recognition and trajectory estimation tasks we achieve equivalent performance to the SOTA, all also utilizing the same pre-trained model. Lastly, we demonstrate the system's potential for 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。
