Multi-robot mapping with neural implicit representations enables the compact reconstruction of complex environments. However, it demands robustness against communication challenges like packet loss and limited bandwidth. While prior works have introduced various mechanisms to mitigate communication disruptions, performance degradation still occurs under extremely low communication success rates. This paper presents UDON, a real-time multi-agent neural implicit mapping framework that introduces a novel uncertainty-weighted distributed optimization to achieve high-quality mapping under severe communication deterioration. The uncertainty weighting prioritizes more reliable portions of the map, while the distributed optimization isolates and penalizes mapping disagreement between individual pairs of communicating agents. We conduct extensive experiments on standard benchmark datasets and real-world robot hardware. We demonstrate that UDON significantly outperforms existing baselines, maintaining high-fidelity reconstructions and consistent scene representations even under extreme communication degradation (as low as 1% success rate).

翻译：多机器人神经隐式映射能够以紧凑方式重建复杂环境，但需具备应对丢包、带宽受限等通信挑战的鲁棒性。尽管现有研究已引入多种机制缓解通信中断问题，但在极低通信成功率条件下仍会出现性能退化。本文提出UDON框架，这是一种实时多智能体神经隐式映射框架，通过引入新型不确定性加权分布式优化，在严重通信恶化场景下实现高质量建图。其中不确定性加权机制优先映射中更可靠的部分，而分布式优化则隔离并惩罚通信智能体对之间的映射不一致性。我们在标准基准数据集和真实机器人硬件上开展了广泛实验。结果表明，即使通信条件极度退化（低至1%成功率），UDON仍显著优于现有基线方法，能够保持高保真重建与一致场景表征。