Encyclopedia of Robotics

Living Edition
| Editors: Marcelo H Ang, Oussama Khatib, Bruno Siciliano

Omnidirectional Vision

  • Helder AraujoEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-642-41610-1_101-1
  • 9 Downloads

Synonyms

Definitions

Omnidirectional vision deals with the processing and extraction of information from images with large fields of view. Usually by omnidirectional vision, it is meant images with (horizontal/vertical) fields of view of no less than 180 (continuous or not) and (vertical/horizontal) fields of view of no less than 40 (continuous or not).

Overview

Omnidirectional vision is useful for many applications, namely, virtual and mixed reality, wide area surveillance, videoconferencing, and robotics (especially mobile robotics). A wide field of view is especially relevant for mobile robotics since it facilitates localization, navigation, mapping, and obstacle detection (Zheng and Tsuji, 1992; Yagi et al., 1994, 1995). Simultaneous Localisation And Mapping (SLAM) can also benefit from omnidirectional images since most methods need features and textured images. In environments with low texture, sparse...

This is a preview of subscription content, log in to check access.

References

  1. Baker S, Nayar SK (1998) A theory of catadioptric image formation. In: Sixth international conference on computer vision, pp 35–42Google Scholar
  2. Baker S, Nayar S (1999) A theory of single-viewpoint catadioptric image formation. Int J Comput Vis 35(2):175–196CrossRefGoogle Scholar
  3. Baker P, Fermuller C, Aloimonos Y, Pless R (2001) A spherical eye from multiple cameras (makes better models of the world). In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, CVPR 2001, vol 1Google Scholar
  4. Bakstein H, Pajdla T (2001) An overview of non-central cameras. In: Proceedings of sixth computer vision winter workshopGoogle Scholar
  5. Barreto J, Araujo H (2001) Issues on the geometry of central catadioptric image formation. In: Proceedings of the 2001 IEEE conference on computer vision and pattern recognition, CVPR 2001, vol 2Google Scholar
  6. Caruso D, Engel J, Cremers D (2015) Large-scale direct SLAM for omnidirectional cameras. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 141–148Google Scholar
  7. Chahl J, Srinivasan M (1997) Reflective surfaces for panoramic imaging. Appl Opt 36(31):8275–8285CrossRefGoogle Scholar
  8. Gamallo C, Mucientes M, Regueiro CV (2015) Omnidirectional visual SLAM under severe occlusions. Robot Auton Syst 65:76–87CrossRefGoogle Scholar
  9. Gaspar J, Decco C, Okamoto J, Santos-Victor J (2002) Constant resolution omnidirectional cameras. In: Proceedings of the IEEE workshop on omnidirectional vision 2002, pp 27–34Google Scholar
  10. Geyer C, Daniilidis K (2000) A unifying theory for central panoramic systems and practical implications. In: Computer vision – ECCV 2000. Springer, pp 445–461Google Scholar
  11. Gohl P, Honegger D, Omari S, Achtelik M, Pollefeys M, Siegwart R (2015) Omnidirectional visual obstacle detection using embedded FPGA. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 3938–3943Google Scholar
  12. Greguss P (1986) Panoramic imaging block for three-dimensional space. US patent US4566763AGoogle Scholar
  13. Grossberg M, Nayar S (2001) A general imaging model and a method for finding its parameters. In: Proceedings of eighth IEEE international conference on computer vision, ICCV 2001, vol 2, pp 108–115Google Scholar
  14. Hicks R, Perline R (2002) Equi-areal catadioptric sensors. In: Proceedings of the IEEE workshop on omnidirectional vision 2002, pp 13–18Google Scholar
  15. Janai J, Güney F, Behl A, Geiger A (2017) Computer vision for autonomous vehicles: problems, datasets and state-of-the-art. CoRR abs/1704.05519Google Scholar
  16. Lee G, Faundorfer F, Pollefeys M (2013) Motion estimation for self-driving cars with a generalized camera. In: 2013 IEEE conference on computer vision and pattern recognition, pp 2746–2753Google Scholar
  17. Lukierski R, Leutenegger S, Davison A (2015) Rapid free-space mapping from a single omnidirectional camera. In: 2015 European conference on mobile robots (ECMR), pp 1–8Google Scholar
  18. Matsuki H, von Stumberg L, Usenko V, Stueckler J, Cremers D (2018) Omnidirectional DSO: direct sparse Odometry with fisheye cameras. IEEE Robot Autom Lett 3(4):3693–3700CrossRefGoogle Scholar
  19. Nayar SK, Peri V (1999) Folded catadioptric cameras. In: Proceedings of 1999 IEEE computer society conference on computer vision and pattern recognition (Cat. No PR00149), vol 2, p 223Google Scholar
  20. Swaminathan R, Grossberg M, Nayar S (2001) Caustics of catadioptric cameras. In: Proceedings of eighth IEEE international conference on computer vision. ICCV 2001, vol 2, pp 2–9Google Scholar
  21. Swaminathan R, Grossberg M, Nayar S (2006) Non-single viewpoint catadioptric cameras: geometry and analysis. Int J Comput Vis 66(3):211–229CrossRefGoogle Scholar
  22. Yagi Y, Kawato S, Tsuji S (1994) Real-time omnidirectional image sensor (COPIS) for vision-guided navigation. IEEE Trans Robot Autom 10(1):11–22CrossRefGoogle Scholar
  23. Yagi Y, Nishizawa Y, Yachida M (1995) Map-based navigation for a mobile robot with omnidirectional image sensor COPIS. IEEE Trans Robot Autom 11(5):634–648CrossRefGoogle Scholar
  24. Zhang Z, Rebecq H, Forster C, Scaramuzza D (2016) Benefit of large field-of-view cameras for visual odometry. In: 2016 IEEE international conference on robotics and automation (ICRA), pp 801–808Google Scholar
  25. Zheng Y, Tsuji S (1992) Panoramic representation for route recognition by a mobile robot. Int J Comput Vis 9(1):55–76CrossRefGoogle Scholar
  26. Zhou F, Chai X, Chen X, Song Y (2016) Omnidirectional stereo vision sensor based on single camera and catoptric system. Appl Opt 55(25):6813–6820CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of Electrical and Computer Engineering, Institute of Systems and RoboticsUniversity of CoimbraCoimbraPortugal

Section editors and affiliations

  • François Chaumette
    • 1
  1. 1.Inria, Univ Rennes, CNRS, IRISARennesFrance