人类通过扩展现实协作提升机器人语义建图精度
Human Interaction for Collaborative Semantic SLAM using Extended Reality
- 人机共用扩展现实环境实时交互，动态添加房间等语义信息
- 在真实工地数据集上，房间识别准确率与地图完整性显著提升
- 适合需要高精度语义地图的智能建造、巡检等场景
语义同步定位与建图（Semantic SLAM）系统为机器人地图增添结构与语义信息，使其在复杂环境中更高效运行。然而，在存在遮挡、数据不全或几何模糊的真实场景中，这些系统难以充分运用人类自然具备的高级空间与语义知识。本文提出 HICS-SLAM，一种基于共享扩展现实环境的人机协同语义建图框架。该系统使操作员可直接可视化机器人构建的三维场景图，并实时添加如房间或结构体等高层语义概念。我们设计了一种基于图的语义融合方法，将人类干预与机器人感知信息整合，实现可扩展的协作以增强态势感知。在真实建筑工地数据集上的实验表明，相较于自动基线，本方法在房间检测准确率、地图精度和语义完整性方面均有显著提升，验证了该方法的有效性及其未来扩展潜力。
原文摘要 · Abstract (English)
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.
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