Robust Graph Matching through Semantic Relationship Generation for SLAM

2026-04-28

AI Summary

This work addresses the challenge of pose ambiguity in indoor environments with repetitive or symmetric layouts, where conventional graph-matching approaches relying solely on geometric structure often fail. To overcome this limitation, the authors propose a semantic-enhanced graph matching method that explicitly models semantic relationships between RGB-D detected objects and structural elements such as rooms and walls. These semantic cues are leveraged to pre-filter matching candidates, which are subsequently refined through geometric verification to improve SLAM robustness. Integrated into the iS-Graphs framework, the approach constructs scene graphs and performs semantic relation reasoning, substantially reducing the number of candidate matches in both synthetic and simulated environments. This leads to notable gains in computational efficiency and convergence speed, with clear performance advantages over purely geometric methods in symmetric scenes.

Abstract

Graph-based representations such as Scene Graphs enable localization in structured indoor environments by matching a locally observed graph, constructed from sensor data, to a prior map. This process is particularly challenging in environments with repetitive or symmetric layouts, where structural cues alone are often insufficient to resolve ambiguities. We propose a semantic-enhanced graph matching approach that explicitly models relations between detected objects and structural elements, such as rooms and wall planes. Objects are detected from RGB-D data and integrated into the graph, and their relations to structural elements are exploited to filter candidate correspondences prior to geometric verification, significantly reducing ambiguity and search complexity. The proposed method is integrated within the iS-Graphs framework and evaluated in synthetic and simulated environments. Results show that semantic relations significantly reduce the number of candidate matches, improve computational efficiency, and enable faster convergence, particularly in symmetric scenarios where purely geometric approaches fail.

Problem

• Graph Matching
• Semantic Relationships
• SLAM
• Ambiguity Resolution
• Indoor Localization

Innovation

• semantic graph matching
• scene graphs
• SLAM
• object-structure relations
• ambiguity reduction