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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.19016 |
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| _version_ | 1866929491156140032 |
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| author | Zhang, Yaojie Luo, Haowen Wang, Weijun Feng, Wei |
| author_facet | Zhang, Yaojie Luo, Haowen Wang, Weijun Feng, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19016 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robust Multi-Robot Global Localization with Unknown Initial Pose based on Neighbor Constraints Zhang, Yaojie Luo, Haowen Wang, Weijun Feng, Wei Robotics 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. |
| title | Robust Multi-Robot Global Localization with Unknown Initial Pose based on Neighbor Constraints |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.19016 |