Rheos: Modelling Continuous Motion Dynamics in Hierarchical 3D Scene Graphs
Iacopo Catalano, Francesco Verdoja, Javier Civera, Jorge Pe\~na-Queralta, Julio A. Placed

TL;DR
Rheos introduces a novel framework embedding continuous, probabilistic motion models into hierarchical 3D scene graphs, significantly improving dynamic environment understanding for navigation tasks.
Contribution
It presents Rheos, a continuous motion modeling approach with Gaussian mixtures and efficient online updates, advancing beyond prior discrete and less scalable methods.
Findings
Outperforms discrete baselines in multiple spatial resolutions
Effectively models multimodal directional flows with explicit uncertainty
Enables real-time operation with linear per-update complexity
Abstract
3D Scene Graphs (3DSGs) provide hierarchical, multi-resolution abstractions that encode the geometric and semantic structure of an environment, yet their treatment of dynamics remains limited to tracking individual agents. Maps of Dynamics (MoDs) complement this by modeling aggregate motion patterns, but rely on uniform grid discretizations that lack semantic grounding and scale poorly. We present Rheos, a framework that explicitly embeds continuous directional motion models into an additional dynamics layer of a hierarchical 3DSG that enhances the navigational properties of the graph. Each dynamics node maintains a semi-wrapped Gaussian mixture model that captures multimodal directional flow as a principled probability distribution with explicit uncertainty, replacing the discrete histograms used in prior work. To enable online operation, Rheos employs reservoir sampling for…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Autonomous Vehicle Technology and Safety · Multimodal Machine Learning Applications
