在通信极差时仍能实现多机器人高精度地图重建。
UDON: Uncertainty-weighted Distributed Optimization for Multi-Robot Neural Implicit Mapping under Extreme Communication Constraints
- 用不确定性加权优化，优先保留可靠地图区域。
- 在1%通信成功率下仍保持高质量地图重建。
- 适合通信受限的机器人协同导航场景。
基于神经隐式表示的多机器人建图可高效重构复杂环境，但对通信中断（如丢包、带宽受限）敏感。现有方法在极端低通信成功率下性能仍会下降。本文提出UDON框架，通过不确定加权分布式优化，在严重通信恶化条件下实现高质量建图。该方法通过不确定性权重聚焦可靠地图区域，并通过分布式优化惩罚通信双方之间的映射差异。在标准基准数据集与真实机器人硬件上实验表明，即使通信成功率低至1%，UDON仍能维持高保真度重建和一致的场景表征，显著优于现有基线方法。
原文摘要 · Abstract (English)
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).
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