ML Anthology

The Registration Problem Revisited: Optimal Solutions from Points, Lines and Planes

Carl Olsson, Fredrik Kahl, Magnus Oskarsson
CVPR 2006 pp. 1206-1213
doi:10.1109/CVPR.2006.307 /cvpr/2006/olsson2006cvpr-registration/

Abstract

In this paper we propose a practical and efficient method for finding the globally optimal solution to the problem of pose estimation of a known object. We present a framework that allows us to use both point-to-point, point-to-line and point-to-plane correspondences in the optimization algorithm. Traditional methods such as the iterative closest point algorithm may get trapped in local minima due to the non-convexity of the problem, however, our approach guarantees global optimality. The approach is based on ideas from global optimization theory, in particular, convex under-estimators in combination with branch and bound. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data.

PDF CVPR Semantic Scholar

Cite

Text

Olsson et al. "The Registration Problem Revisited: Optimal Solutions from Points, Lines and Planes." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.307

Markdown

[Olsson et al. "The Registration Problem Revisited: Optimal Solutions from Points, Lines and Planes." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/olsson2006cvpr-registration/) doi:10.1109/CVPR.2006.307

BibTeX

@inproceedings{olsson2006cvpr-registration,
  title     = {{The Registration Problem Revisited: Optimal Solutions from Points, Lines and Planes}},
  author    = {Olsson, Carl and Kahl, Fredrik and Oskarsson, Magnus},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2006},
  pages     = {1206-1213},
  doi       = {10.1109/CVPR.2006.307},
  url       = {https://mlanthology.org/cvpr/2006/olsson2006cvpr-registration/}
}