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[2409.11972] Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks
Summary
This paper introduces a learning-based approach for generating uncertainty-aware high-level spatial concepts in 3D Scene Graphs, enhancing SLAM systems for robotics.
Why It Matters
The research addresses the limitations of manual specification in SLAM systems, which can hinder scalability and generalization. By automating the generation of spatial concepts, this work has the potential to improve indoor navigation and mapping in diverse environments, making it significant for advancements in robotics and AI applications.
Key Takeaways
- Introduces a novel method for inferring spatial concepts from geometric observations.
- Eliminates the need for manual design in SLAM systems, enhancing efficiency.
- Demonstrates significant improvements in room detection and trajectory estimation.
- Validates the approach in both simulated and real-world environments.
- Sets a foundation for extending the method to new spatial concepts.
Computer Science > Robotics arXiv:2409.11972 (cs) [Submitted on 18 Sep 2024 (v1), last revised 15 Feb 2026 (this version, v3)] Title:Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks Authors:Jose Andres Millan-Romera, Muhammad Shaheer, Miguel Fernandez-Cortizas, Martin R. Oswald, Holger Voos, Jose Luis Sanchez-Lopez View a PDF of the paper titled Generation of Uncertainty-Aware High-Level Spatial Concepts in Factorized 3D Scene Graphs via Graph Neural Networks, by Jose Andres Millan-Romera and 5 other authors View PDF HTML (experimental) Abstract: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 present...
