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2024

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

Zhong, Daoxin, Robinson, Luke, De Martini, Daniele

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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.

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

Built on

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P. Corke and S. Hutchinson, “A new partitioned approach to image-based visual servo control,” IEEE Transactions on Robotics and Automation , vol. 17, no. 4, pp. 507–515, 2001
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B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” 2020. [Online]. Available: https://arxiv.org/abs/2003.08934
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2003
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N. Garcia-Aracil, C. Perez-Vidal, J. M. Sabater, R. Morales, and F. J. Badesa, “Robust and cooperative image-based visual servoing system using a redundant architecture,” Sensors , vol. 11, no. 12, p. 11885–11900, Dec. 2011. [Online]. Available: http://dx.doi.org/10.3390/s111211885
2011
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J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” 2015. [Online]. Available: https://arxiv.org/abs/1506.02640
Original
2015
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L. Yen-Chen, P. Florence, J. T. Barron, A. Rodriguez, P. Isola, and T.-Y. Lin, “iNeRF: Inverting neural radiance fields for pose estimation,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021
2021
Cited alongside, same era.
L. Robinson, M. Gadd, P. Newman, and D. De Martini, “Robot-relay: Building-wide, calibration-less visual servoing with learned sensor handover network,” arXiv preprint arXiv:2310.15677 , 2023
Original
2023
Cited alongside, same era.

Then

L. Robinson, D. De Martini, M. Gadd, and P. Newman, “Visual servoing on wheels: Robust robot orientation estimation in remote viewpoint control,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 6364–6370
2023
Later among the works it cites.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, and R. Girshick, “Segment anything,” arXiv:2304.02643 , 2023
Original
2023
Later among the works it cites.
M. Tancik, E. Weber, E. Ng, R. Li, B. Yi, J. Kerr, T. Wang, A. Kristoffersen, J. Austin, K. Salahi, A. Ahuja, D. McAllister, and A. Kanazawa, “Nerfstudio: A modular framework for neural radiance field development,” 2023. [Online]. Available: https://arxiv.org/abs/2302.04264
Original
2023
Later among the works it cites.
S. Wang, V. Leroy, Y. Cabon, B. Chidlovskii, and J. Revaud, “Dust3r: Geometric 3d vision made easy,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 20 697–20 709

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