Robots Get Better at Understanding Their Surroundings in Any Location

<p>3D scene graph (3DSG) generation is a rapidly evolving field that plays a significant role in robotic autonomy. Traditionally, the focus has been on indoor environments, where robots understand and navigate spaces by abstracting objects and geometric information in a structured graph format. Expanding upon this idea, this paper introduces a 3DSG construction architecture, which enables scene-agnostic abstraction of the environment, with the goal of facilitating the adoption of 3DSG for autonomous agents in both indoor and outdoor environments. We propose a novel approach for area delimitation in 3DSGs that leverages label propagation to cluster entities (i.e. objects of interest) into areas that are both semantically and topologically distinguishable within a scene. Towards this end, we establish label propagation for 3DSGs, by formulating a dynamic set of propagation factors that accommodate to the relevance of semantic information and their natural decay through the topological structure of the 3DSG. Additionally, to achieve scene-agnostic area delimitation, we introduce a single-step optimization process for the calculation of clutter-aware propagation factors based on the approximation of an optimal set of factors that maximize inter-area eccentricity while minimizing intra-area eccentricity. Finally, the proposed framework is extensively validated through simulations and real-world deployments using a Boston Dynamics Spot legged robot and a Clearpath Husky mobile robot. The experimental results showcase the scalability of the proposed framework to indoor and outdoor environments for real-time 3DSG construction.</p>