Multi-Robot Object SLAM Using Distributed Variational Inference
<|reference_start|>Multi-Robot Object SLAM Using Distributed Variational Inference: Multi-robot simultaneous localization and mapping (SLAM) enables a robot team to achieve coordinated tasks by relying on a common map of the environment. Constructing a map by centralized processing of the robot observations is undesirable because it creates a single point of failure and requires pre-existing infrastructure and significant communication throughput. This paper formulates multi-robot object SLAM as a variational inference problem over a communication graph subject to consensus constraints on the object estimates maintained by different robots. To solve the problem, we develop a distributed mirror descent algorithm with regularization enforcing consensus among the communicating robots. Using Gaussian distributions in the algorithm, we also derive a distributed multi-state constraint Kalman filter (MSCKF) for multi-robot object SLAM. Experiments on real and simulated data show that our method improves the trajectory and object estimates, compared to individual-robot SLAM, while achieving better scaling to large robot teams, compared to centralized multi-robot SLAM.<|reference_end|>
@article{cao2024multi-robot,
    title={Multi-Robot Object SLAM Using Distributed Variational Inference},
    author={Hanwen Cao, Sriram Shreedharan and Nikolay Atanasov},
    journal={arXiv preprint arXiv:2404.18331},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18331},
    primaryClass={cs.RO}
}