标题： 基于扩展现实的协作语义SLAM的人机交互 显示英文标题

标题： Human Interaction for Collaborative Semantic SLAM using Extended Reality

摘要： 语义SLAM（同时定位与地图构建）系统通过结构和语义信息丰富机器人地图，使机器人在复杂环境中更有效地运行。 然而，在存在遮挡、数据不完整或几何模糊的现实场景中，这些系统表现不佳，因为它们无法充分利用人类自然应用的高级空间和语义知识。 我们引入了HICS-SLAM，这是一种人机协同的语义SLAM框架，使用共享的扩展现实环境进行实时协作。 该系统允许人类操作员直接与机器人的三维场景图进行交互并进行可视化，并将高层语义概念（例如房间或结构实体）添加到地图构建过程中。 我们提出了一种基于图的语义融合方法，将这些人工干预与机器人感知相结合，实现了可扩展的协作以增强情境意识。 在真实建筑工地数据集上的实验评估表明，与自动化基线相比，房间检测准确性、地图精度和语义完整性都有所提高，证明了该方法的有效性及其未来扩展的潜力。 显示英文摘要

摘要： Semantic SLAM (Simultaneous Localization and Mapping) systems enrich robot maps with structural and semantic information, enabling robots to operate more effectively in complex environments. However, these systems struggle in real-world scenarios with occlusions, incomplete data, or ambiguous geometries, as they cannot fully leverage the higher-level spatial and semantic knowledge humans naturally apply. We introduce HICS-SLAM, a Human-in-the-Loop semantic SLAM framework that uses a shared extended reality environment for real-time collaboration. The system allows human operators to directly interact with and visualize the robot's 3D scene graph, and add high-level semantic concepts (e.g., rooms or structural entities) into the mapping process. We propose a graph-based semantic fusion methodology that integrates these human interventions with robot perception, enabling scalable collaboration for enhanced situational awareness. Experimental evaluations on real-world construction site datasets demonstrate improvements in room detection accuracy, map precision, and semantic completeness compared to automated baselines, demonstrating both the effectiveness of the approach and its potential for future extensions.