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arXiv · 2408.01251

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

Daoxin Zhong·Luke Robinson·Daniele De Martini
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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 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.

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Daoxin Zhong, Luke Robinson, Daniele De Martini. 2024-08-02. NeRFoot: Robot-Footprint Estimation for Image-Based Visual Servoing. https://arxiv.org/abs/2408.01251

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