让机器人在户外环境构建带语义的3D场景图，提升自主决策能力。
Towards Terrain-Aware Task-Driven 3D Scene Graph Generation in Outdoor Environments
- 融合度量与语义信息生成户外点云，支持复杂场景理解。
- 改进室内方法适配户外，实现结构化场景建模。
- 为真实野外机器人应用提供可推理的环境表示。
高层自主操作依赖于机器人对环境构建足够丰富的模型。传统三维场景表示如点云和占用网格虽提供详细几何信息，但缺乏高阶语义组织，难以支撑高层推理。3D场景图（3DSGs）通过将几何、拓扑和语义关系整合为多层次图结构，弥补此缺陷。它能捕捉物体与空间布局的分层抽象，使机器人得以结构化地理解环境，从而提升情境感知决策与自适应规划能力。尽管现有研究多聚焦室内场景，本文探索3DSG在户外环境中的构建与实用性。提出一种方法生成适用于大型户外场景的任务无关度量-语义点云，并对现有室内3DSG生成技术进行修改以适配户外场景。初步定性结果验证了户外3DSG的可行性，凸显其在未来野外机器人应用中的潜力。
原文摘要 · Abstract (English)
High-level autonomous operations depend on a robot's ability to construct a sufficiently expressive model of its environment. Traditional three-dimensional (3D) scene representations, such as point clouds and occupancy grids, provide detailed geometric information but lack the structured, semantic organization needed for high-level reasoning. 3D scene graphs (3DSGs) address this limitation by integrating geometric, topological, and semantic relationships into a multi-level graph-based representation. By capturing hierarchical abstractions of objects and spatial layouts, 3DSGs enable robots to reason about environments in a structured manner, improving context-aware decision-making and adaptive planning. Although most recent work has focused on indoor 3DSGs, this paper investigates their construction and utility in outdoor environments. We present a method for generating a task-agnostic metric-semantic point cloud for large outdoor settings and propose modifications to existing indoor 3DSG generation techniques for outdoor applicability. Our preliminary qualitative results demonstrate the feasibility of outdoor 3DSGs and highlight their potential for future deployment in real-world field robotic applications.
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