用图神经网络自动发现3D场景中的房间等空间概念，提升机器人导航精度。
Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks
- 通过图神经网络从垂直平面中在线学习高阶空间概念
- 仿真环境房检测提升20.7%，轨迹估计提升19.2%
- 无需人工设计规则，适合复杂室内场景的自主建图
让机器人从原始几何观测（如平面）中自主发现高阶空间概念（如房间、墙）是实现鲁棒室内导航与建图的关键。因子化3D场景图以分层度量-语义方式组织这些概念，并将其作为优化因子约束相对几何关系、保证全局一致性，从而提升graph-SLAM性能。然而，当前方法仍高度依赖人工：概念生成使用手工设计的特定启发式规则，因子及其协方差也需手动设定。这限制了在多样化环境中的泛化能力及对新概念类别的扩展性。本文提出一种新型基于学习的方法，可在线从观测到的垂直平面推断空间概念，并将其作为可优化因子引入SLAM后端，无需人工设计概念生成、因子结构和协方差。在复杂布局的仿真环境中评估，房检测准确率提升20.7%，轨迹估计精度提高19.2%；在真实建筑工地验证，房检测提升5.3%，地图匹配准确率提升3.8%。
原文摘要 · Abstract (English)
Enabling robots to autonomously discover high-level spatial concepts (e.g., rooms and walls) from primitive geometric observations (e.g., planar surfaces) within 3D Scene Graphs is essential for robust indoor navigation and mapping. These graphs provide a hierarchical metric-semantic representation in which such concepts are organized. To further enhance graph-SLAM performance, Factorized 3D Scene Graphs incorporate these concepts as optimization factors that constrain relative geometry and enforce global consistency. However, both stages of this process remain largely manual: concepts are typically derived using hand-crafted, concept-specific heuristics, while factors and their covariances are likewise manually designed. This reliance on manual specification limits generalization across diverse environments and scalability to new concept classes. This paper presents a novel learning-based method that infers spatial concepts online from observed vertical planes and introduces them as optimizable factors within a SLAM backend, eliminating the need to handcraft concept generation, factor design, and covariance specification. We evaluate our approach in simulated environments with complex layouts, improving room detection by 20.7% and trajectory estimation by 19.2%. Validated on real construction sites, room detection improves by 5.3% and map matching accuracy by 3.8%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。