Resource-Aware Collaborative Monte Carlo Localization with Distribution Compression
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909157631721472 |
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| author | Zimmerman, Nicky Giusti, Alessandro Guzzi, Jérôme |
| author_facet | Zimmerman, Nicky Giusti, Alessandro Guzzi, Jérôme |
| contents | Global localization is essential in enabling robot autonomy, and collaborative localization is key for multi-robot systems. In this paper, we address the task of collaborative global localization under computational and communication constraints. We propose a method which reduces the amount of information exchanged and the computational cost. We also analyze, implement and open-source seminal approaches, which we believe to be a valuable contribution to the community. We exploit techniques for distribution compression in near-linear time, with error guarantees. We evaluate our approach and the implemented baselines on multiple challenging scenarios, simulated and real-world. Our approach can run online on an onboard computer. We release an open-source C++/ROS2 implementation of our approach, as well as the baselines |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02010 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Resource-Aware Collaborative Monte Carlo Localization with Distribution Compression Zimmerman, Nicky Giusti, Alessandro Guzzi, Jérôme Robotics Global localization is essential in enabling robot autonomy, and collaborative localization is key for multi-robot systems. In this paper, we address the task of collaborative global localization under computational and communication constraints. We propose a method which reduces the amount of information exchanged and the computational cost. We also analyze, implement and open-source seminal approaches, which we believe to be a valuable contribution to the community. We exploit techniques for distribution compression in near-linear time, with error guarantees. We evaluate our approach and the implemented baselines on multiple challenging scenarios, simulated and real-world. Our approach can run online on an onboard computer. We release an open-source C++/ROS2 implementation of our approach, as well as the baselines |
| title | Resource-Aware Collaborative Monte Carlo Localization with Distribution Compression |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.02010 |