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Robust Multi-Robot Global Localization with Unknown Initial Pose based on Neighbor Constraints

Authors :
Zhang, Yaojie
Luo, Haowen
Wang, Weijun
Feng, Wei
Source :
ICRA 2024
Publication Year :
2024

Abstract

Multi-robot global localization (MR-GL) with unknown initial positions in a large scale environment is a challenging task. The key point is the data association between different robots' viewpoints. It also makes traditional Appearance-based localization methods unusable. Recently, researchers have utilized the object's semantic invariance to generate a semantic graph to address this issue. However, previous works lack robustness and are sensitive to overlap rate of maps, resulting in unpredictable performance in real-world environments. In this paper, we propose a data association algorithm based on neighbor constraints to improve the robustness of the system. We demonstrate the effectiveness of our method on three different datasets, indicating a significant improvement in robustness compared to previous works.<br />Comment: 7 pages (6+1), accepted by ICRA 2024

Subjects

Subjects :
Computer Science - Robotics

Details

Database :
arXiv
Journal :
ICRA 2024
Publication Type :
Report
Accession number :
edsarx.2406.19016
Document Type :
Working Paper
Full Text :
https://doi.org/10.1109/ICRA57147.2024.10610066