Federated Multi-Agent Mapping for Planetary Exploration
Tiberiu-Ioan Szatmari Abhishek Cauligi
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 networks,
for compact and adaptable representations. We further enhance this approach
with meta-initialization on Earth datasets, pre-training the network to quickly
learn new map structures. This combination demonstrates strong generalization
to diverse domains like Martian terrain and glaciers. We rigorously evaluate
this approach, demonstrating its effectiveness for real-world deployment in
multi-agent exploration scenarios.