Effective environment perception is crucial for enabling downstream robotic applications. Individual robotic agents often face occlusion and limited visibility issues, whereas multi-agent systems can offer a more comprehensive mapping of the environment, quicker coverage, and increased fault tolerance. In this paper, we propose a collaborative multi-agent perception system where agents collectively learn a neural radiance field (NeRF) from posed RGB images to represent a scene. Each agent processes its local sensory data and shares only its learned NeRF model with other agents, reducing communication overhead. Given NeRF's low memory footprint, this approach is well-suited for robotic systems with limited bandwidth, where transmitting all raw data is impractical. Our distributed learning framework ensures consistency across agents' local NeRF models, enabling convergence to a unified scene representation. We show the effectiveness of our method through an extensive set of experiments on datasets containing challenging real-world scenes, achieving performance comparable to centralized mapping of the environment where data is sent to a central server for processing. Additionally, we find that multi-agent learning provides regularization benefits, improving geometric consistency in scenarios with sparse input views. We show that in such scenarios, multi-agent mapping can even outperform centralized training.

翻译：高效的环境感知对于实现下游机器人应用至关重要。单个机器人智能体常面临遮挡与视野受限问题，而多智能体系统能够提供更全面的环境建图、更快的覆盖速度以及更高的容错能力。本文提出一种协作式多智能体感知系统，其中多个智能体通过位姿已知的RGB图像协同学习神经辐射场（NeRF）以表征场景。每个智能体处理本地传感器数据，仅将学习得到的NeRF模型与其他智能体共享，从而显著降低通信开销。鉴于NeRF模型具有较低的内存占用，该方法特别适用于带宽受限的机器人系统，因为在此类系统中传输所有原始数据是不现实的。我们的分布式学习框架确保了各智能体本地NeRF模型的一致性，使其能够收敛到统一的场景表征。通过在包含复杂真实场景的数据集上进行大量实验，我们验证了该方法的有效性：其性能可与将数据发送至中央服务器处理的集中式环境建图相媲美。此外，我们发现多智能体学习具有正则化优势，在输入视角稀疏的场景中能提升几何一致性。研究表明在此类场景下，多智能体建图甚至能够超越集中式训练的表现。