This paper investigates the utility of Neural Radiance Fields (NeRF) models in extending the regions of operation of a mobile robot, controlled by Image-Based Visual Servoing (IBVS) via static CCTV cameras. Using NeRF as a 3D-representation prior, the robot's footprint may be extrapolated geometrically and used to train a CNN-based network to extract it online from the robot's appearance alone. The resulting footprint results in a tighter bound than a robot-wide bounding box, allowing the robot's controller to prescribe more optimal trajectories and expand its safe operational floor area.

翻译：本文研究了神经辐射场（NeRF）模型在扩展移动机器人操作区域方面的应用，该机器人通过静态闭路电视摄像头进行基于图像的视觉伺服（IBVS）控制。利用NeRF作为三维表示先验，机器人的足迹可通过几何方法外推，并用于训练一个基于CNN的网络，使其仅根据机器人的外观在线提取足迹。所得的足迹比覆盖整个机器人的边界框约束更紧密，从而使机器人控制器能够规划更优的轨迹，并扩大其安全操作的地面区域。