Robust Multi-Robot Global Localization with Unknown Initial Pose based on Neighbor Constraints
<|reference_start|>Robust Multi-Robot Global Localization with Unknown Initial Pose based on Neighbor Constraints: 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.<|reference_end|>
@article{zhang2024robust,
    title={Robust Multi-Robot Global Localization with Unknown Initial Pose based
  on Neighbor Constraints},
    author={Yaojie Zhang, Haowen Luo, Weijun Wang, Wei Feng},
    journal={ICRA 2024},
    year={2024},
    doi={10.1109/ICRA57147.2024.10610066},
    archivePrefix={arXiv},
    eprint={2406.19016},
    primaryClass={cs.RO}
}