Resource-Aware Collaborative Monte Carlo Localization with Distribution Compression
<|reference_start|>Resource-Aware Collaborative Monte Carlo Localization with Distribution Compression: 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<|reference_end|>
@article{zimmerman2024resource-aware,
    title={Resource-Aware Collaborative Monte Carlo Localization with Distribution
  Compression},
    author={Nicky Zimmerman, Alessandro Giusti, J'er^ome Guzzi},
    journal={arXiv preprint arXiv:2404.02010},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02010},
    primaryClass={cs.RO}
}