NeRFoot: Robot-Footprint Estimation for Image-Based Visual Servoing
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912056264884224 |
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| author | Zhong, Daoxin Robinson, Luke De Martini, Daniele |
| author_facet | Zhong, Daoxin Robinson, Luke De Martini, Daniele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01251 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NeRFoot: Robot-Footprint Estimation for Image-Based Visual Servoing Zhong, Daoxin Robinson, Luke De Martini, Daniele Robotics 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. |
| title | NeRFoot: Robot-Footprint Estimation for Image-Based Visual Servoing |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.01251 |