  This paper presents MULAN-WC, a novel multi-robot 3D reconstruction framework
that leverages wireless signal-based coordination between robots and Neural
Radiance Fields (NeRF). Our approach addresses key challenges in multi-robot 3D
reconstruction, including inter-robot pose estimation, localization uncertainty
quantification, and active best-next-view selection. We introduce a method for
using wireless Angle-of-Arrival (AoA) and ranging measurements to estimate
relative poses between robots, as well as quantifying and incorporating the
uncertainty embedded in the wireless localization of these pose estimates into
the NeRF training loss to mitigate the impact of inaccurate camera poses.
Furthermore, we propose an active view selection approach that accounts for
robot pose uncertainty when determining the next-best views to improve the 3D
reconstruction, enabling faster convergence through intelligent view selection.
Extensive experiments on both synthetic and real-world datasets demonstrate the
effectiveness of our framework in theory and in practice. Leveraging wireless
coordination and localization uncertainty-aware training, MULAN-WC can achieve
high-quality 3d reconstruction which is close to applying the ground truth
camera poses. Furthermore, the quantification of the information gain from a
novel view enables consistent rendering quality improvement with incrementally
captured images by commending the robot the novel view position. Our hardware
experiments showcase the practicality of deploying MULAN-WC to real robotic
systems.
