NeRFoot: Robot-Footprint Estimation for Image-Based Visual Servoing
| Autor: | Zhong, Daoxin, Robinson, Luke, De Martini, Daniele |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Accepted as extended abstract for ICRA@40 |
| Databáze: | arXiv |
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