Distributed Optimization with Consensus Constraint for Multi-Robot Semantic Octree Mapping
<|reference_start|>Distributed Optimization with Consensus Constraint for Multi-Robot Semantic Octree Mapping: This work develops a distributed optimization algorithm for multi-robot 3-D semantic mapping using streaming range and visual observations and single-hop communication. Our approach relies on gradient-based optimization of the observation log-likelihood of each robot subject to a map consensus constraint to build a common multi-class map of the environment. This formulation leads to closed-form updates which resemble Bayes rule with one-hop prior averaging. To reduce the amount of information exchanged among the robots, we utilize an octree data structure that compresses the multi-class map distribution using adaptive-resolution.<|reference_end|>
@article{asgharivaskasi2024distributed,
    title={Distributed Optimization with Consensus Constraint for Multi-Robot
  Semantic Octree Mapping},
    author={Arash Asgharivaskasi, Nikolay Atanasov},
    journal={arXiv preprint arXiv:2402.08867},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08867},
    primaryClass={cs.RO}
}