# Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment

arXiv: https://arxiv.org/abs/2010.09297 · HTML: https://mlsanity.com/paper/2010.09297/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [Deep Depth Completion from Extremely Sparse Data: A Survey](https://mlsanity.com/paper/2205.05335/) (2022)
- [Boosting Light-Weight Depth Estimation Via Knowledge Distillation](https://mlsanity.com/paper/2105.06143/) (2021)
- [An Object SLAM Framework for Association, Mapping, and High-Level Tasks](https://mlsanity.com/paper/2305.07299/) (2023)
- [Dense Depth Distillation with Out-of-Distribution Simulated Images](https://mlsanity.com/paper/2208.12464/) (2022)
- [CLIP-Clique: Graph-based Correspondence Matching Augmented by Vision Language Models for Object-based Global Localization](https://mlsanity.com/paper/2410.03054/) (2024)
- [Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching](https://mlsanity.com/paper/2201.05977/) (2022)
- [AirLoc: Object-based Indoor Relocalization](https://mlsanity.com/paper/2304.00954/) (2023)
- [A Novel Image Descriptor with Aggregated Semantic Skeleton Representation for Long-term Visual Place Recognition](https://mlsanity.com/paper/2202.03677/) (2022)
- [Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency](https://mlsanity.com/paper/2304.05146/) (2023)
- [Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer](https://mlsanity.com/paper/2403.10552/) (2024)
- [SWA-PF: Semantic-Weighted Adaptive Particle Filter for Memory-Efficient 4-DoF UAV Localization in GNSS-Denied Environments](https://mlsanity.com/paper/2509.13795/) (2025)
- [MSG-Loc: Multi-Label Likelihood-based Semantic Graph Matching for Object-Level Global Localization](https://mlsanity.com/paper/2512.03522/) (2025)
- [Abnormal Occupancy Grid Map Recognition using Attention Network](https://mlsanity.com/paper/2110.09047/) (2021)
- [Progressive Self-Distillation for Ground-to-Aerial Perception Knowledge Transfer](https://mlsanity.com/paper/2208.13404/) (2022)
- [CMSG Cross-Media Semantic-Graph Feature Matching Algorithm for Autonomous Vehicle Relocalization](https://mlsanity.com/paper/2305.08318/) (2023)
- [Single-Shot Global Localization via Graph-Theoretic Correspondence Matching](https://mlsanity.com/paper/2306.03641/) (2023)
- [Cross-view Self-localization from Synthesized Scene-graphs](https://mlsanity.com/paper/2310.15504/) (2023)

## Abstract (arXiv)

The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL). The difficulties are two-fold. The first is the difficulty of global localization for significant viewpoint difference. Appearance-based localization methods tend to fail under large viewpoint changes. Recently, semantic graphs have been utilized to overcome the viewpoint variation problem. However, the methods are highly time-consuming, especially in large-scale environments. This leads to the second difficulty, which is how to perform real-time global localization. In this paper, we propose a semantic histogram-based graph matching method that is robust to viewpoint variation and can achieve real-time global localization. Based on that, we develop a system that can accurately and efficiently perform MR-GL for both homogeneous and heterogeneous robots. The experimental results show that our approach is about 30 times faster than Random Walk based semantic descriptors. Moreover, it achieves an accuracy of 95% for global localization, while the accuracy of the state-of-the-art method is 85%.