Machine Vision and Applications

, Volume 19, Issue 2, pp 73–83 | Cite as

Measuring and modelling sewer pipes from video

  • Juho KannalaEmail author
  • Sami S. Brandt
  • Janne Heikkilä
Original Paper


This article presents a system for the automatic measurement and modelling of sewer pipes. The system recovers the interior shape of a sewer pipe from a video sequence which is acquired by a fish-eye lens camera moving inside the pipe. The approach is based on tracking interest points across successive video frames and posing the general structure-from-motion problem. It is shown that the tracked points can be reliably reconstructed despite the forward motion of the camera. This is achieved by utilizing a fish-eye lens with a wide field of view. The standard techniques for robust estimation of the two- and three-view geometry are modified so that they can be used for calibrated fish-eye lens cameras with a field of view less than 180°. The tubular arrangement of the reconstructed points allows pipe shape estimation by surface fitting. Hence, a method for modelling such surfaces with a locally cylindrical model is proposed. The system is demonstrated with a real sewer video and an error analysis for the recovered structure is presented.


Structure from motion 3D-reconstruction Omnidirectional vision Visual inspection Modelling 


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  1. 1.
    Ahn S.J., Rauh W., Cho H.S. and Warnecke H.J. (2002). Orthogonal distance fitting of implicit curves and surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 24(5): 620–638 CrossRefGoogle Scholar
  2. 2.
    Brandt, S.S.: On the probabilistic epipolar geometry. In: Proc. BMVC, pp. 107–116 (2004)Google Scholar
  3. 3.
    Burschka, D., Li, M., Taylor, R., Hager, G.D.: Scale-invariant registration of monocular endoscopic images to ct-scans for sinus surgery. In: Proc. MICCAI, pp. 413–421 (2004)Google Scholar
  4. 4.
    Chae M.J. and Abraham D.M. (2001). Neuro-fuzzy approaches for sanitary sewer pipeline condition assessment. J. Comput. Civil Eng. 15(1): 4–14 CrossRefGoogle Scholar
  5. 5.
    Cooper D., Pridmore T.P. and Taylor N. (1998). Towards the recovery of extrinsic camera parameters from video records of sewer surveys. Mach. Vis. Appl. 11: 53–63 CrossRefGoogle Scholar
  6. 6.
    Csurka G., Zeller C., Zhang Z. and Faugeras O. (1997). Characterizing the uncertainty of the fundamental matrix. Comput. Vis. Image Underst. 68(1): 18–36 CrossRefGoogle Scholar
  7. 7.
    Faber, P., Fisher, B.: A buyer’s guide to euclidean elliptical cylindrical and conical surface fitting. In: Proc. BMVC, pp. 521–530 (2001)Google Scholar
  8. 8.
    Fitzgibbon A., Pilu M. and Fisher R.B. (1999). Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell. 21(5): 476–480 CrossRefGoogle Scholar
  9. 9.
    Fitzgibbon, A.W., Zisserman, A.: Automatic 3D model acquisition and generation of new images from video sequences. In: Proc. European Signal Processing Conference, pp. 1261–1269 (1998)Google Scholar
  10. 10.
    Fitzgibbon, A.W., Zisserman, A.: Automatic camera recovery for closed or open image sequences. In: Proc. ECCV, pp. 311–326 (1998)Google Scholar
  11. 11.
    Forsyth, D., Ponce, J.: Computer Vision, a Modern Approach. Prentice Hall (2003)Google Scholar
  12. 12.
    Geyer, C., Daniilidis, K.: Structure and motion from uncalibrated catadioptric views. In: Proc. CVPR, pp. 279–286 (2001)Google Scholar
  13. 13.
    Gooch, R.M., Clarke, T.A., Ellis, T.J.: A semi-autonomous sewer surveillance and inspection vehicle. In: Proc. IEEE Intelligent Vehicles, pp. 64–69 (1996)Google Scholar
  14. 14.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference (1988)Google Scholar
  15. 15.
    Hartley, R., Zisserman, A.: Multiple View Geometry, 2 edn., Cambridge (2003)Google Scholar
  16. 16.
    Kannala, J.: Measuring the shape of sewer pipes from video. Master’s thesis, TKK (2004)Google Scholar
  17. 17.
    Kannala, J., Brandt, S.S.: Measuring the shape of sewer pipes from video. In: Proc. MVA (2005)Google Scholar
  18. 18.
    Kannala J. and Brandt S.S. (2006). A generic camera model and calibration method for conventional, wide-angle and fish-eye lenses. IEEE Trans. Pattern Anal. Mach. Intell. 28(8): 1335–1340 CrossRefGoogle Scholar
  19. 19.
    Kraus K. (1997). Photogrammetry, vol. 2: Advanced methods and applications. Ferd. Dummler’s, Verlag Bonn Google Scholar
  20. 20.
    Kuntze, H.B., Haffner, H.: Experiences with the development of a robot for smart multisensoric pipe inspection. In: Proc. IEEE Robotics and Automation, pp. 1773–1778 (1998)Google Scholar
  21. 21.
    Lhuillier, M.: Automatic structure and motion using a catadioptric camera. In: Proc. OMNIVIS (2005)Google Scholar
  22. 22.
    Lhuillier, M., Perriollat, M.: Uncertainty ellipsoids calculations for complex 3D reconstructions. In: Proc. IEEE Robotics and Automation, pp. 3062–3069 (2006)Google Scholar
  23. 23.
    Lhuillier M. and Quan L. (2005). A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Trans. Pattern Anal. Mach. Intell. 27(3): 418–433 CrossRefGoogle Scholar
  24. 24.
    Lukács, G., Martin, R., Marshall, D.: Faithful least-squares fitting of spheres, cylinders, cones and tori for reliable segmentation. In: Proc. ECCV, pp. 671–686 (1998)Google Scholar
  25. 25.
    Mičušík B. and Pajdla T. (2006). Structure from motion with wide circular field of view cameras. IEEE Trans. Pattern Anal. Mach. Intell. 28(7): 1135–1149 CrossRefGoogle Scholar
  26. 26.
    Nister D. (2004). An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 26(6): 756–770 CrossRefGoogle Scholar
  27. 27.
    Schmidt, J., Vogt, F., Niemann, H.: Nonlinear refinement of camera parameters using an endoscopic surgery robot. In: Proc. MVA, pp. 40–43 (2002)Google Scholar
  28. 28.
    Sinha S.K. and Fieguth P.W. (2006). Morphological segmentation and classification of underground pipe images. Mach. Vis. Appl. 17: 21–31 CrossRefGoogle Scholar
  29. 29.
    Triggs B., McLauchlan P.F., Hartley R. and Fitzgibbon A. (2000). Bundle adjustment—a modern synthesis. Lecture Notes Comput. Sci. 1883: 298–372 CrossRefGoogle Scholar
  30. 30.
    Umeyama S. (1991). Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. 13(4): 376–380 CrossRefGoogle Scholar
  31. 31.
    Werghi, N., Fisher, R., Robertson, C., Ashbrook, A.: Modelling objects having quadric surfaces incorporating geometric constraints. In: Proc. ECCV, pp. 185–201 (1998)Google Scholar
  32. 32.
    Xu, G., Zhang, Z.: Epipolar Geometry in Stereo, Motion and Object Recognition. Kluwer (1996)Google Scholar
  33. 33.
    Xu K., Luxmoore A.R. and Davies T. (1998). Sewer pipe deformation assessment by image analysis of video surveys. Pattern 31(2): 169–180 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Juho Kannala
    • 1
    Email author
  • Sami S. Brandt
    • 1
    • 2
  • Janne Heikkilä
    • 1
  1. 1.Machine Vision GroupUniversity of OuluOuluFinland
  2. 2.Laboratory of Computational EngineeringHelsinki University of TechnologyEspooFinland

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