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- Title
Exterior Orientation Revisited: A Robust Method Based on l<sub>q</sub>-norm.
- Authors
Jiayuan Li; Qingwu Hu; Ruofei Zhong; Mingyao Ai
- Abstract
Camera exterior orientation is essential in many photogrammetry and computer vision applications, including 3D reconstruction, digital orthophoto map (DOM) generation, and localization. In this paper, we propose a new formulation of exterior orientation that is robust against gross errors (outliers). Different from classic optimization methods whose cost function is based on the l2-norm of residuals, we use lq-norm (0<q<l) instead. We reformulate the new cost function as an augmented Lagrangian function because it is not strictly convex. In addition, we employ the alternating direction method of multipliers (ADMM) to decompose the augmented Lagrangian function into three simple sub-problems and solve them iteratively. Our work recovers the orientation and position of a camera from outliers contaminating observations without any gross error detection stage such as random sample consensus (RANSAC). Extensive experiments on both synthetic and real data demonstrate that the proposed method significantly outperforms state-of-the-art methods and can easily handle situations with up to 85 percent outliers. The source code of the proposed algorithm is made public.
- Subjects
PHOTOGRAMMETRY; COMPUTER vision; ORTHOPHOTOMAPS; COST functions; ROBUST control
- Publication
International Journal of Applied Sports Sciences, 2016, Vol 28, Issue 2, p47
- ISSN
1598-2939
- Publication type
Article
- DOI
10.14358/PERS.83.1.47