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.

翻译：本文提出了MULAN-WC——一种全新的多机器人三维重建框架，通过无线信号实现机器人间的协同并与神经辐射场（NeRF）相结合。该方法解决了多机器人三维重建中的关键挑战，包括机器人间位姿估计、定位不确定性量化及主动最佳下一视角选择。我们引入了一种利用无线到达角（AoA）和测距测量值来估计机器人间相对位姿的方法，并将这些位姿估计中嵌入的无线定位不确定性量化后融入NeRF训练损失函数，以减轻不准确相机位姿的影响。此外，我们提出了一种主动视角选择方法，在确定下一最佳视角时考虑机器人位姿不确定性，从而通过智能视角选择实现更快的收敛速度。在合成数据集和真实世界数据集上的大量实验从理论和实践层面验证了该框架的有效性。通过无线协同与定位不确定性感知训练，MULAN-WC可获得近乎采用真实相机位姿的高质量三维重建。此外，通过量化新视角的信息增益，向机器人推荐新视角位置可使逐步捕获的图像持续提升渲染质量。硬件实验展示了将MULAN-WC部署到真实机器人系统的实用性。