analiza aiqbench
This research addresses object-level global localization for robots by introducing a multi-label likelihood framework that resolves semantic ambiguity through context-aware graph matching. By leveraging neighbor information to refine node probabilities, the method improves pose estimation accuracy in both closed-set and open-world scenarios with large vocabularies. The work advances autonomous navigation capabilities where traditional single-label approaches struggle due to environmental uncertainty.
MSG-Loc: Multi-Label Likelihood-based Semantic Graph Matching for Object-Level Global Localization
arXiv:2512.03522v3 Announce Type: replace Abstract: Robots are often required to localize in environments with unknown object classes and semantic ambiguity. However, when performing global localization using semantic objects, high semantic ambiguity intensifies object misclassification and increases the likelihood of incorrect associations, which in turn can cause significant errors in the estimated pose. Thus, in this letter, we propose a multi-label likelihood-based semantic graph matching framework for object-level global localization. The key idea is to exploit multi-label graph representations, rather than single-label alternatives, to capture and leverage the inherent semantic context of object observations. Based on these representations, our approach enhances semantic correspondence across graphs by combining the likelihood of each node with the maximum likelihood of its neighbors via context-aware likelihood propagation. For rigorous validation, data association and pose estimation performance are evaluated under both closed-set and open-set detection configurations. In addition, we demonstrate the scalability of our approach to large-vocabulary object categories in both real-world indoor scenes and synthetic environments.
Abstract:Robots are often required to localize in environments with unknown object classes and semantic ambiguity. However, when performing global localization using semantic objects, high semantic ambiguity intensifies object misclassification and increases the likelihood of incorrect associations, which in turn can cause significant errors in the estimated pose. Thus, in this letter, we propose a multi-label likelihood-based semantic graph matching framework for object-level global localization. The key idea is to exploit multi-label graph representations, rather than single-label alternatives, to capture and leverage the inherent semantic context of object observations. Based on these representations, our approach enhances semantic correspondence across graphs by combining the likelihood of each node with the maximum likelihood of its neighbors via context-aware likelihood propagation. For rigorous validation, data association and pose estimation performance are evaluated under both closed-set and open-set detection configurations. In addition, we demonstrate the scalability of our approach to large-vocabulary object categories in both real-world indoor scenes and synthetic environments.
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2512.03522 [cs.RO]
(or arXiv:2512.03522v3 [cs.RO] for this version)
https://doi.org/10.1109/LRA.2025.3643293 DOI(s) linking to related resources
Text integral sub licența deschisă a publicației · 178 cuvinte
Subiecte conexe
J++ Lens: Jacobian Filtering Enables More Faithful Workspace Lenses
LessWrong · 8 oct. 2026
Presentation: Building Evals for AI Adoption: from Principles to Practice
InfoQ · 29 mai 2026
Natural language security queries at Orca Security using Amazon Bedrock
AWS · 13 oct. 2026
Research Note: Filtering Subversion-Relevant Information From Pretraining Data Is Feasible
LessWrong · 5 oct. 2026