Making Minimal Solvers for Absolute Pose Estimation Compact and Robust

Abstract

In this paper we present new techniques for constructing compact and robust minimal solvers for absolute pose estimation. We focus on the P4Pfr problem, but the methods we propose are applicable to a more general setting. Previous approaches to P4Pfr suffer from artificial degeneracies which come from their formulation and not the geometry of the original problem. In this paper we show how to avoid these false degeneracies to create more robust solvers. Combined with recently published techniques for Grobner basis solvers we are also able to construct solvers which are significantly smaller. We evaluate our solvers on both real and synthetic data, and show improved performance compared to competing solvers. Finally we show that our techniques can be directly applied to the P3.5Pf problem to get a non-degenerate solver, which is competitive with the current state-of-the-art.

Cite

Text

Larsson et al. "Making Minimal Solvers for Absolute Pose Estimation Compact and Robust." International Conference on Computer Vision, 2017. doi:10.1109/ICCV.2017.254

Markdown

[Larsson et al. "Making Minimal Solvers for Absolute Pose Estimation Compact and Robust." International Conference on Computer Vision, 2017.](https://mlanthology.org/iccv/2017/larsson2017iccv-making/) doi:10.1109/ICCV.2017.254

BibTeX

@inproceedings{larsson2017iccv-making,
  title     = {{Making Minimal Solvers for Absolute Pose Estimation Compact and Robust}},
  author    = {Larsson, Viktor and Kukelova, Zuzana and Zheng, Yinqiang},
  booktitle = {International Conference on Computer Vision},
  year      = {2017},
  doi       = {10.1109/ICCV.2017.254},
  url       = {https://mlanthology.org/iccv/2017/larsson2017iccv-making/}
}