Robust Graph Matching through Semantic Relationship Generation for SLAM
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910173300260864 |
|---|---|
| author | Perez-Saura, David Millan-Romera, Jose Andres Fernandez-Cortizas, Miguel Voos, Holger Campoy, Pascual Sanchez-Lopez, Jose Luis |
| author_facet | Perez-Saura, David Millan-Romera, Jose Andres Fernandez-Cortizas, Miguel Voos, Holger Campoy, Pascual Sanchez-Lopez, Jose Luis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25404 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust Graph Matching through Semantic Relationship Generation for SLAM Perez-Saura, David Millan-Romera, Jose Andres Fernandez-Cortizas, Miguel Voos, Holger Campoy, Pascual Sanchez-Lopez, Jose Luis Robotics 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. |
| title | Robust Graph Matching through Semantic Relationship Generation for SLAM |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.25404 |