logo

SCIENCE CHINA Information Sciences, Volume 64 , Issue 10 : 209203(2021) https://doi.org/10.1007/s11432-019-9925-1

Minimal solution for estimating fundamental matrix under planar motion

More info
  • ReceivedJan 23, 2019
  • AcceptedJun 19, 2019
  • PublishedJul 20, 2020

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by Fundamental Research Funds for the Central Universities (Grant No. FRF-TP-18-100A1), National Natural Science Foundation of China (Grant No. 61803025), Beijing Science and Technology Project (Grant No. Z181100003118006), and Joint Funds of Equipment Pre-Research and Ministry of Education of China (Grant No. 6141A02033339).


Supplement

Appendixes A–C.


References

[1] Hartley R, Zisserman A. Multiple View Geometry in Computer Vision (2nd ed). Cambridge: Cambridge University Press, 2004. Google Scholar

[2] He W, Li Z J, Chen C L P. A survey of human-centered intelligent robots: issues and challenges. IEEE/CAA J Autom Sin, 2017, 4: 602-609 CrossRef Google Scholar

[3] Choi S, Kim J H. Fast and reliable minimal relative pose estimation under planar motion. Image Vision Computing, 2018, 69: 103-112 CrossRef Google Scholar

[4] Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM, 1981, 24: 381-395 CrossRef Google Scholar

[5] Ortín D, Montiel J M M. Indoor robot motion based on monocular images. Robotica, 2001, 19: 331-342 CrossRef Google Scholar

[6] Kukelova Z, Bujnak M, Pajdla T. Automatic generator of minimal problem solvers. In: Proceedings of the 10th European Conference on Computer Vision, Marseille, 2008. 302-315. Google Scholar

[7] Nister D. An efficient solution to the five-point relative pose problem.. IEEE Trans Pattern Anal Machine Intell, 2004, 26: 756-770 CrossRef PubMed Google Scholar

[8] Booij O, Zivkovic Z. The planar two point algorithm. IAS-UVA-09-05. 2009. Google Scholar

  • Figure 1

    (Color online) (a) Illustration of camera movement; (b) log$_{10}$ of $E_r$ using noise-free data for general 3D scene (planar camera motion); (c) $E_r$ under different noise levels for general 3D scene (planar camera motion); (d) $E_r$ under different noise levels for planar scene (planar camera motion); (e) performance of different methods for increasing non-planar camera motion;protectłinebreak (f) runtime of four different methods with eight-, seven-, six-, and four-point correspondences.