Collaborative mapping of unknown environments can be done faster and more robustly than a single robot. However, a collaborative approach requires a distributed paradigm to be scalable and deal with communication issues. This work presents a fully distributed algorithm enabling a group of robots to collectively optimize the parameters of a Neural Radiance Field (NeRF). The algorithm involves the communication of each robot's trained NeRF parameters over a mesh network, where each robot trains its NeRF and has access to its own visual data only. Additionally, the relative poses of all robots are jointly optimized alongside the model parameters, enabling mapping with unknown relative camera poses. We show that multi-robot systems can benefit from differentiable and robust 3D reconstruction optimized from multiple NeRFs. Experiments on real-world and synthetic data demonstrate the efficiency of the proposed algorithm. See the website of the project for videos of the experiments and supplementary material(https://sites.google.com/view/di-nerf/home).

翻译：未知环境的协同建图比单机器人建图更快速、更鲁棒。然而，协同方法需要分布式范式以实现可扩展性并应对通信问题。本文提出一种完全分布式算法，使一组机器人能够集体优化神经辐射场（NeRF）的参数。该算法涉及每个机器人在网状网络上通信其训练好的NeRF参数，其中每个机器人独立训练自己的NeRF且仅能访问自身视觉数据。此外，所有机器人的相对位姿与模型参数被联合优化，从而实现在相对相机位姿未知情况下的建图。我们证明多机器人系统能够受益于由多个NeRF联合优化的可微分鲁棒三维重建。在真实世界与合成数据上的实验验证了所提算法的有效性。实验视频及补充材料见项目网站（https://sites.google.com/view/di-nerf/home）。