Federated Multi-Agent Mapping for Planetary Exploration

arXiv:2404.02289v1 Announce Type: cross
Abstract: In multi-agent robotic exploration, managing and effectively utilizing the vast, heterogeneous data generated from dynamic environments poses a significant challenge. Federated learning (FL) is a promising approach for distributed mapping, addressing the challenges of decentralized data in collaborative learning. FL enables joint model training across multiple agents without requiring the centralization or sharing of raw data, overcoming bandwidth and storage constraints. Our approach leverages implicit neural mapping, representing maps as continuous functions learned by neural …

abstract agent agents arxiv challenge challenges collaborative cs.lg cs.ma cs.ro data decentralized decentralized data distributed dynamic environments exploration federated learning generated mapping multi-agent multiple robotic training type vast