# MSG-Loc: Multi-Label Likelihood-based Semantic Graph Matching for Object-Level Global Localization

arXiv: https://arxiv.org/abs/2512.03522 · HTML: https://mlsanity.com/paper/2512.03522/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 31 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 24 of 24)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [CubeSLAM: Monocular 3D Object SLAM](https://mlsanity.com/paper/1806.00557/) (2018)
- [QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM](https://mlsanity.com/paper/1804.04011/) (2018)
- [Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively](https://mlsanity.com/paper/2401.02955/) (2024)
- [Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark](https://mlsanity.com/paper/2206.13996/) (2022)
- [A Normalized Gaussian Wasserstein Distance for Tiny Object Detection](https://mlsanity.com/paper/2110.13389/) (2021)
- [X-View: Graph-Based Semantic Multi-View Localization](https://mlsanity.com/paper/1709.09905/) (2017)
- [Tokenize Anything via Prompting](https://mlsanity.com/paper/2312.09128/) (2023)
- [EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association](https://mlsanity.com/paper/2004.12730/) (2020)
- [Monocular Object and Plane SLAM in Structured Environments](https://mlsanity.com/paper/1809.03415/) (2018)
- [Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment](https://mlsanity.com/paper/2010.09297/) (2020)
- [OA-SLAM: Leveraging Objects for Camera Relocalization in Visual SLAM](https://mlsanity.com/paper/2209.08338/) (2022)
- [An Object SLAM Framework for Association, Mapping, and High-Level Tasks](https://mlsanity.com/paper/2305.07299/) (2023)
- [CLIP-Loc: Multi-modal Landmark Association for Global Localization in Object-based Maps](https://mlsanity.com/paper/2402.06092/) (2024)
- [GOReloc: Graph-based Object-Level Relocalization for Visual SLAM](https://mlsanity.com/paper/2408.07917/) (2024)
- [VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks](https://mlsanity.com/paper/2402.13609/) (2024)
- [Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models](https://mlsanity.com/paper/2412.19920/) (2024)
- [CLIP-Clique: Graph-based Correspondence Matching Augmented by Vision Language Models for Object-based Global Localization](https://mlsanity.com/paper/2410.03054/) (2024)
- [SemanticTopoLoop: Semantic Loop Closure With 3D Topological Graph Based on Quadric-Level Object Map](https://mlsanity.com/paper/2311.02831/) (2023)

## Built on by (most cited first, 1 of 1)

- [VideoReloc: Long-Term Indoor Video Relocalization against a Kilobyte-Scale Semantic Scene Graph](https://mlsanity.com/paper/2609.21804/) (2026)

## Abstract (arXiv)

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. Project Page: https://sparolab.github.io/research/msg-loc/.