Autonomous navigation in dynamic environments requires spatial representations that capture both semantic structure and temporal evolution. 3D Scene Graphs (3DSGs) provide hierarchical multi-resolution abstractions that encode geometry and semantics, but existing extensions toward dynamics largely focus on individual objects or agents. In parallel, Maps of Dynamics (MoDs) model typical motion patterns and temporal regularities, yet are usually tied to grid-based discretizations that lack semantic awareness and do not scale well to large environments. In this paper we introduce Aion, a framework that embeds temporal flow dynamics directly within a hierarchical 3DSG, effectively incorporating the temporal dimension. Aion employs a graph-based sparse MoD representation to capture motion flows over arbitrary time intervals and attaches them to navigational nodes in the scene graph, yielding more interpretable and scalable predictions that improve planning and interaction in complex dynamic environments. We provide the code at https://github.com/IacopomC/aion

翻译：动态环境中的自主导航需要既能捕捉语义结构又能反映时间演化的空间表征。3D场景图（3DSG）提供了编码几何与语义信息的分层多分辨率抽象，但现有的动力学扩展主要聚焦于单个物体或智能体。与此同时，动力学地图（MoD）虽能建模典型运动模式与时间规律，却通常与缺乏语义感知且难以扩展至大型环境的网格离散化方法绑定。本文提出Aion框架，将时间流动态直接嵌入分层3DSG中，有效纳入了时间维度。Aion采用基于图的稀疏MoD表征来捕捉任意时间间隔内的运动流，并将其附着于场景图中的导航节点，从而在复杂动态环境中生成更可解释且可扩展的预测，进而提升规划与交互能力。相关代码已开源至https://github.com/IacopomC/aion