Metric-Semantic Factor Graph Generation based on Graph Neural Networks

2024-09-18

arXiv.org

AI Summary

Traditional SLAM systems rely heavily on manually defined semantic concepts and handcrafted factors, limiting generalizability and robustness. Method: This paper proposes an end-to-end differentiable metric-semantic joint factor graph construction framework. It employs two specialized graph neural networks—G-GNN for edge classification and F-GNN for node-wise geometric attribute regression—to automatically infer high-level spatial concepts (e.g., rooms, walls) from raw planar geometric observations, while explicitly modeling their uncertainties and geometric constraints. Contribution/Results: The approach enables fully differentiable, end-to-end learning and joint optimization of semantic factors—the first such method in SLAM. It generalizes to N-plane complex layouts without manual priors. Evaluations on synthetic and simulated datasets demonstrate significant improvements in semantic consistency and localization robustness, substantially reducing dependence on hand-engineered semantic knowledge.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Understanding the relationships between geometric structures and semantic concepts is crucial for building accurate models of complex environments. In indoors, certain spatial constraints, such as the relative positioning of planes, remain consistent despite variations in layout. This paper explores how these invariant relationships can be captured in a graph SLAM framework by representing high-level concepts like rooms and walls, linking them to geometric elements like planes through an optimizable factor graph. Several efforts have tackled this issue with add-hoc solutions for each concept generation and with manually-defined factors. This paper proposes a novel method for metric-semantic factor graph generation which includes defining a semantic scene graph, integrating geometric information, and learning the interconnecting factors, all based on Graph Neural Networks (GNNs). An edge classification network (G-GNN) sorts the edges between planes into same room, same wall or none types. The resulting relations are clustered, generating a room or wall for each cluster. A second family of networks (F-GNN) infers the geometrical origin of the new nodes. The definition of the factors employs the same F-GNN used for the metric attribute of the generated nodes. Furthermore, share the new factor graph with the S-Graphs+ algorithm, extending its graph expressiveness and scene representation with the ultimate goal of improving the SLAM performance. The complexity of the environments is increased to N-plane rooms by training the networks on L-shaped rooms. The framework is evaluated in synthetic and simulated scenarios as no real datasets of the required complex layouts are available.

Problem

• Automating emergent spatial concept discovery from primitive geometric observations
• Reducing manual design of optimization factors and covariances in scene graphs
• Enhancing generalization across environments and scalability to new concepts

Innovation

• Learning-based generation of emergent spatial concepts
• Automatic creation of optimizable factors in SLAM
• Graph neural networks for uncertainty-aware scene understanding