Multi-Robot Autonomous Exploration and Mapping Under Localization Uncertainty with Expectation-Maximization
<|reference_start|>Multi-Robot Autonomous Exploration and Mapping Under Localization Uncertainty with Expectation-Maximization: We propose an autonomous exploration algorithm designed for decentralized multi-robot teams, which takes into account map and localization uncertainties of range-sensing mobile robots. Virtual landmarks are used to quantify the combined impact of process noise and sensor noise on map uncertainty. Additionally, we employ an iterative expectation-maximization inspired algorithm to assess the potential outcomes of both a local robot's and its neighbors' next-step actions. To evaluate the effectiveness of our framework, we conduct a comparative analysis with state-of-the-art algorithms. The results of our experiments show the proposed algorithm's capacity to strike a balance between curbing map uncertainty and achieving efficient task allocation among robots.<|reference_end|>
@article{huang2024multi-robot,
    title={Multi-Robot Autonomous Exploration and Mapping Under Localization
  Uncertainty with Expectation-Maximization},
    author={Yewei Huang, Xi Lin, Brendan Englot},
    journal={arXiv preprint arXiv:2403.04021},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04021},
    primaryClass={cs.RO}
}