Omni-directional vision system with fibre grating device for obstacle detection

Author(s): K. Gurkahraman ; E. Unsal ; Y. Cebi
DOI: 10.1049/iet-cvi.2010.0057

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Author(s): K. Gurkahraman ¹ ; E. Unsal ¹ ; Y. Cebi ¹
- Affiliations: 1: Department of Computer Engineering, Dokuz Eylul University, Buca, Turkey
Source: Volume 5, Issue 5, September 2011, p. 267 – 281
DOI: 10.1049/iet-cvi.2010.0057 , Print ISSN 1751-9632, Online ISSN 1751-9640

In this study, a new omni-directional vision system is presented for localisation and wide field of view (FOV) mapping of the environment. The vision system includes two charge coupled device (CCD) cameras fitted in front of two rectilinear mirrors to sense the environment in a stereo manner. In order to obtain the points representing the obstacles in the environment, a dot-matrix laser pattern created by a fibre grating device (FGD) was used. With the help of the developed mathematical and error estimation models, the distances between the points on the objects and the vision system were determined; and by using synthetic data, the effects of noise on the error rates were analysed. Although the error rates of X-, Y- and Z-axis were increased according to the distance between the obstacle and the vision system for the same horizontal/vertical plane, the errors for X (range) and Z (height) were decreased with the increasing distance between the vision system and horizontal/vertical planes for real world. The main reasons of errors were the size and location of the laser points, reflection errors on the mirrors, sensitivity of the refractive lenses, alignment of the mirror–camera pairs and limitation of the image resolution.

References

1. 1)
  - Su, L., Luo, C., Zhu, F.: `Obtaining obstacle information by an omnidirectional stereo vision system', IEEE Int. Conf. on Information Acquisition, 2006, p. 48–52.
2. 2)
  - Southwell, D., Basu, A., Fiala, M., Reyda, J.: `Panoramic stereo', Proc. Int. Conf. on Pattern Recognition, 1996, 1, p. 378–382.
3. 3)
  - Gluckman, J., Nayar, S.K.: `Planar catadioptric stereo: geometry and calibration', IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 1999, 1.
4. 4)
  - Nakazawa, K., Suzuki, C.: `Development of 3-D robot vision sensor with fiber grating: fusion of 2-D intensity image and discrete range image', Proc. IEEE Conf. of Industrial Electronics, Control and Instrumentation (IECON '91), 1991, 3, p. 2368–2372.
5. 5)
  - Kweon, G., Kim, K., Choi, Y., Kim, G., Kim, H., Yang, S.: `A catadioptric double-panoramic lens with the equi-distance projection for a rangefinder application', Proc. SPIE, 2004, Bellingham, WA, 5613.
6. 6)
  - Chang, Y., Kuwabara, H., Yamamoto, Y.: `Novel application of a laser range finder with vision system for wheeled mobile robot', IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, July 2008, p. 280–285.
7. 7)
  - Dellaert, F., Seitz, S.M., Thorpe, C.E., Thrun, S.: `Structure from motion without correspondence', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2000, 2, p. 557–564.
8. 8)
  - Wang, J.H., Hsiao, C.P.: `Stereo matching by neural network that uses Sobel feature data', IEEE Int. Conf. on Neural Networks, 1996, 3, p. 1801–1806.
9. 9)
  - D.J. Kriegman , E. Triend , T.O. Binford . Stereo vision and navigation in buildings for mobile robots. IEEE Trans. Robot. Autom. , 792 - 803
10. 10)
  - Z. Zhang . Determining epipolar geometry and its uncertainty: a review. Int. J. Comput. Vis. , 2 , 161 - 195
11. 11)
  - Gasparri, A., Panzieri, S., Pascucci, F., Ulivi, G.: `A hybrid active global localisation algorithm for mobile robots', IEEE Int. Conf. on Robotics and Automation, April 2007, p. 3148–3153.
12. 12)
  - R. Larson , R.P. Hostetler , B.H. Edwards . (1998) Calculus.
13. 13)
  - Correa, F.R., Guizilini, V.C., Junior, J.O.: `Omnidirectional stereovision system with two-lobe hyperbolic mirror for robot navigation', ABCM Symp. Ser. Mechatronics, 2006, 2, p. 653–660.
14. 14)
  - S. Baker , S. Nayar . A theory of single-viewpoint catadioptric image formation. Int. J. Comput. Vis. , 2 , 1 - 22
15. 15)
  - J. Kim , Y. Suga . An omnidirectional vision-based moving obstacle detection in mobile robot. Int. J. Control Autom. Syst. , 6 , 663 - 673
16. 16)
  - G. Kweon , S. Hwang-bo , G. Kim , S. Yang , Y. Lee . Wide-angle catadioptric lens with a rectilinear projection scheme. Appl. Opt. , 34 , 8659 - 8673
17. 17)
  - Gluckman, J., Nayar, S.K.: `Rectified catadioptric stereo sensors', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2000, 2, p. 380–387.
18. 18)
  - T.C.S. Azevedo , J.M.R.S. Tavares , M.A.P. Vaz . 3D object reconstruction from uncalibrated images using an off-the-shelf camera. Adv. Comput. Vis. Med. Image Process. Methods Appl. Sci. , 117 - 136
19. 19)
  - Nene, S.A., Nayar, S.K.: `Stereo with mirrors', Proc. Sixth Int. Conf. on Computer Vision, January 1998, p. 1087–1094.
20. 20)
  - R. Orghidan , E.M. Mouaddib , J. Salvi , J.J. Serrano . Catadioptric single-shot rangefinder for textured map building in robot navigation. IET Comput. Vis. , 2 , 43 - 53
21. 21)
  - Tamagawa, K., Ogawa, K., Nakajima, M.: `Detection of respiratory movement during SPECT/PET data acquisition', IEEE Nuclear Science Symp. Conf. Record, 2002, 3, p. 1571–1574.
22. 22)
  - J.P. Siebert , S.J. Marshall . Human body 3D imaging by speckle texture projection photogrammetry. Sens. Rev. , 3 , 218 - 226
23. 23)
  - T.C.S. Azevedo , J.M.R.S. Tavares , M.A.P. Vaz . Three-dimensional reconstruction and characterization of human external shapes from two-dimensional images using volumetric methods. Comput. Methods Biomech. Biomed. Eng. , 3 , 359 - 369
24. 24)
  - Unsal, E.: `Obtaining the three-dimensional structure of current environment by using fiber grating laser system', 2010, Thesis report of Master of Science, Graduate School of Natural and Applied Sciences of Dokuz Eylul University.
25. 25)
  - O. Faugeras . (1999) Three-dimensional computer vision: a geometric viewpoint.
26. 26)
  - Duan, Z., Cai, Z.: `Robust simultaneous localization and mapping based on laser range finder with improved adaptive particle filter', Control and Decision Conf., (CCDC), July 2008, p. 2820–2824.
27. 27)
  - T. McInerney , D. Terzopoluos . Deformable models in medical image analysis: a survey. Med. Image Anal. , 2 , 91 - 108
28. 28)
  - Svoboda, T., Pajdla, T., Hlavac, V.: `Epipolar geometry for panoramic cameras', Fifth European Conf. on Computer Vision, 1998, p. 218–232.
29. 29)
  - R. Hartley . (2003) Multiple view geometry in computer vision.
30. 30)
  - Nayar, S.: `Catadioptric omnidirectional camera', Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1997, p. 482–488.
31. 31)
  - Orghidan, R., Mouaddib, E.M., Salvi, J.: `Omnidirectional depth computation from a single image', Proc. 2005 IEEE Int. Conf. on Robotics and Automation, 2005, p. 1222–1227.
32. 32)
  - M.K. Habib . Fiber-grating-based vision system for real-time tracking, monitoring and obstacle detection. IEEE Sens. J. , 105 - 121
33. 33)
  - Yamaguchi, J., Nakajima, M.: `A 3D shape identification system using a fiber grating vision sensor', Proc. IEEE Conf. of Industrial Electronics, Control and Instrumentation (IECON '90), 1990, 1, p. 507–511.

Omni-directional vision system with fibre grating device for obstacle detection

Omni-directional vision system with fibre grating device for obstacle detection

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