Making Minimal Solvers for Absolute Pose Estimation Compact and Robust

Viktor Larsson, Zuzana Kukelova, Yinqiang Zheng

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

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 Gröbner 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.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Pages2335-2343
Number of pages9
ISBN (Electronic)9781538610329
DOIs
Publication statusPublished - 2017 Dec 22
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: 2017 Oct 222017 Oct 29

Conference

Conference16th IEEE International Conference on Computer Vision, ICCV 2017
Country/TerritoryItaly
CityVenice
Period2017/10/222017/10/29

Subject classification (UKÄ)

  • Computer Vision and Robotics (Autonomous Systems)

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