NeRFoot: Robot-Footprint Estimation for Image-Based Visual Servoing

Abstract-- 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 3Drepresentation 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. Visual servoing is a robotics technique that provides control Figure 1: [4] controls the robot based on its bounding box (yellow) based on visual feedback from external cameras. When checking if a trajectory [1], the field has evolved to encompass various methodologies is safe, its box must stay within the drivable region (blue).