NeRFoot: Robot-Footprint Estimation for Image-Based Visual Servoing
<|reference_start|>NeRFoot: Robot-Footprint Estimation for Image-Based Visual Servoing: 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.<|reference_end|>
@article{zhong2024nerfoot:,
    title={NeRFoot: Robot-Footprint Estimation for Image-Based Visual Servoing},
    author={Daoxin Zhong, Luke Robinson and Daniele De Martini},
    journal={arXiv preprint arXiv:2408.01251},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01251},
    primaryClass={cs.RO}
}