Relative pose estimation for multiple cameras using Lie algebra optimization.
Abstract
This paper addresses the problem of relative pose estimation for multiple cameras in the context of motion-based camera calibration. Relative pose is found from a set of camera-target relative poses. A least-square kind loss function is established using 3D relative Riemannian metrics of targets and cameras. Lie algebra-based solvers are proposed for orientation and pose estimation. Multiple cameras are calibrated without common targets or features in space using our proposed framework. The simulation and real data evaluation demonstrate that our proposed algorithms can achieve high accuracy with limited and precise camera-target poses.
Keywords
Access
DOI:
10.1364/AO.58.002963
Citation
ID:
72144
References
Blockchain Verification
Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
72144
Unique Identifier:
10.1364/AO.58.002963
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
Creates 1,000,000 NFT tokens for this article
Token Features:
- ERC-1155 Standard NFT
- 1 Million Supply per Article
- Transferable via MetaMask
- Permanent Blockchain Record
Scan with Saymatik Web3.0 Wallet