Conference item
SiLVR: scalable Lidar-visual reconstruction with neural radiance fields for robotic inspection
- Abstract:
- We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photo-realistic textures. This system adapts the state-of-the-art neural radiance field (NeRF) representation to also incorporate lidar data which adds strong geometric constraints on the depth and surface normals. We exploit the trajectory from a real-time lidar SLAM system to bootstrap a Structure-from-Motion (SfM) procedure to both significantly reduce the computation time and to provide metric scale which is crucial for lidar depth loss. We use submapping to scale the system to large-scale environments captured over long trajectories. We demonstrate the reconstruction system with data from a multi-camera, lidar sensor suite onboard a legged robot, hand-held while scanning building scenes for 600 metres, and onboard an aerial robot surveying a multi-storey mock disaster site-building. Website: https://ori-drs.github.io/projects/silvr/
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 4.4MB, Terms of use)
-
- Publisher copy:
- 10.1109/ICRA57147.2024.10611278
Authors
+ European Commission
More from this funder
- Funder identifier:
- https://ror.org/00k4n6c32
- Grant:
- 101070405
- Programme:
- Horizon Europe project Digiforest
+ UK Research and Innovation
More from this funder
- Funder identifier:
- https://ror.org/001aqnf71
- Grant:
- 10037847
- Publisher:
- IEEE
- Host title:
- 2024 IEEE International Conference on Robotics and Automation (ICRA)
- Pages:
- 17983-17989
- Publication date:
- 2024-08-08
- Acceptance date:
- 2024-01-29
- Event title:
- 2024 IEEE International Conference on Robotics and Automation (ICRA 2024)
- Event location:
- Yokohama, Japan
- Event website:
- https://2024.ieee-icra.org/
- Event start date:
- 2024-05-13
- Event end date:
- 2024-05-17
- DOI:
- EISBN:
- 9798350384574
- ISBN:
- 9798350384581
- Language:
-
English
- Keywords:
- Pubs id:
-
1804139
- Local pid:
-
pubs:1804139
- Deposit date:
-
2024-03-14
- ARK identifier:
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2024
- Rights statement:
- © 2024 IEEE.
- Notes:
-
This is the accepted manuscript version of the paper. The final version is available online from IEEE at https://dx.doi.org/10.1109/ICRA57147.2024.10611278
This project has been partly funded by the Horizon Europe project Digiforest (101070405). Maurice Fallon is supported by a Royal Society University Research Fellowship. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) license to any Accepted Manuscript version arising.
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record