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Absolute Orientation from Uncertain Point Data: A Unified Approach

Yacov Hel-Or, Michael Werman
CVPR 1992 pp. 77-82
doi:10.1109/CVPR.1992.223224 /cvpr/1992/helor1992cvpr-absolute/

Abstract

A general and flexible method for fusing and integrating different 2D and 3D measurements for pose estimation is proposed. The 2D measured data are viewed as 3D data with infinite uncertainty in a particular direction. This representation unifies the two categories of the absolute orientation problem into a single problem that varies only in the uncertainty values associated with the measurements. With this paradigm a uniform mathematical formulation of the problem is obtained, and different kinds of measurements that can be fused to obtain a better solution. The method, which is implemented using Kalman filtering, is robust and easily parallelizable.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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Cite

Text

Hel-Or and Werman. "Absolute Orientation from Uncertain Point Data: A Unified Approach." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1992. doi:10.1109/CVPR.1992.223224

Markdown

[Hel-Or and Werman. "Absolute Orientation from Uncertain Point Data: A Unified Approach." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1992.](https://mlanthology.org/cvpr/1992/helor1992cvpr-absolute/) doi:10.1109/CVPR.1992.223224

BibTeX

@inproceedings{helor1992cvpr-absolute,
  title     = {{Absolute Orientation from Uncertain Point Data: A Unified Approach}},
  author    = {Hel-Or, Yacov and Werman, Michael},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1992},
  pages     = {77-82},
  doi       = {10.1109/CVPR.1992.223224},
  url       = {https://mlanthology.org/cvpr/1992/helor1992cvpr-absolute/}
}