  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).
