Absolute and Relative Pose Estimation of a Multi-View Camera System using 2D-3D Line Pairs and Vertical Direction (bibtex)
by Hichem Abdellali, Zoltan Kato
Abstract:
We propose a new algorithm for estimating the absolute and relative pose of a multi-view camera system. The algorithm relies on two solvers: a direct solver using a minimal set of 6 line pairs and a least squares solver which uses all inlier 2D-3D line pairs. The algorithm have been validated on a large synthetic dataset, experimental results confirm the stable and real-time performance under realistic noise on the line parameters as well as on the vertical direction. Furthermore, the algorithm performs well on real data with less then half degree rotation error and less than 25 cm translation error.
Reference:
Hichem Abdellali, Zoltan Kato, Absolute and Relative Pose Estimation of a Multi-View Camera System using 2D-3D Line Pairs and Vertical Direction, In Proceedings of International Conference on Digital Image Computing: Techniques and Applications, Canberra, Australia, pp. 1-8, 2018, IEEE.
Bibtex Entry:
@string{dicta="Proceedings of International Conference on Digital Image Computing: Techniques and Applications"}
@InProceedings{Abdellali2018,
  author =	 {Hichem Abdellali and Zoltan Kato},
  title =	 {Absolute and Relative Pose Estimation of a
                  Multi-View Camera System using 2D-3D Line Pairs and
                  Vertical Direction},
  booktitle =	 dicta,
  year =	 2018,
  pages =	 {1-8},
  address =	 {Canberra, Australia},
  month =	 dec,
  publisher =	 {IEEE},
  doi       = {10.1109/dicta.2018.8615792},
  abstract =	 {We propose a new algorithm for estimating the
                  absolute and relative pose of a multi-view camera
                  system. The algorithm relies on two solvers: a
                  direct solver using a minimal set of 6 line pairs
                  and a least squares solver which uses all inlier
                  2D-3D line pairs. The algorithm have been validated
                  on a large synthetic dataset, experimental results
                  confirm the stable and real-time performance under
                  realistic noise on the line parameters as well as on
                  the vertical direction. Furthermore, the algorithm
                  performs well on real data with less then half
                  degree rotation error and less than 25 cm
                  translation error.}
}
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