UDON: Uncertainty-weighted Distributed Optimization for Multi-Robot Neural Implicit Mapping under Extreme Communication Constraints

Abstract

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).

Figures (6)

• Figure 1: UDON outperforms existing baselines in a three-robot mapping experiment conducted with a challenging 5% communication success rate. We conducted the experiment using a dataset collected with TurtleBot robots. For visualization purposes, the trajectory of each agent is color-coded. While the baseline methods either fail to converge or produce incomplete reconstructions, UDON, in contrast, successfully fuses information from all agents to create a complete map of the scene.
• Figure 2: Qualitative comparison on ScanNet scene0169 with a challenging 1% communication rate. The agent trajectories are color-coded on the ground truth mesh for reference. Both Di-NeRF and RAMEN completely fail to reconstruct the scene. In contrast, MACIM and UDON both produce coherent maps, demonstrating the significant benefit of even minimal communication over a no-communication baseline (map learned from only blue agent's observations). However, a closer inspection (highlighted in orange) reveals UDON's superior ability to capture fine details, accurately reconstructing the chairs against the wall which MACIM misses.
• Figure 3: Comparison of reconstruction quality on scene0000 across four communication success rates (1%, 5%, 10%, 20%). UDON (ours) achieves the lowest artifacts and holes and the highest completion ratio, with small variance. MACIM performs reasonably but falls behind UDON as communication degrades, while Di-NeRF and RAMEN collapse. These results highlight UDON’s robustness under constrained communication.
• Figure 4: Comparison of reconstruction quality on scene0000 with varying numbers of communicating agents (4, 6, 8) at 5% communication success rate. UDON (ours) consistently outperforms MACIM. Furthermore, reconstruction quality improves as the number of participating agents increases, indicating that even under low communication rates, leveraging more agents provides clear benefits.
• Figure 5: Qualitative comparison on ScanNet scene0000 with an increasing number of agents (4, 6, 8 agents) at 5% communication success rate. As more agents are added, UDON effectively leverages the additional data to produce increasingly complete scene reconstructions. Compared to MACIM, UDON demonstrates a significant advantage in areas observed by only a few agents (highlighted regions, color-coded by the corresponding agent's trajectory). Furthermore, UDON captures color and texture with higher fidelity as evidenced by the accurately-reconstructed floor tiles.