Autonomous Mapping of the Priscilla Catacombs

  • Frank Verbiest
  • Marc Proesmans
  • Luc Van Gool


This chapter describes the image-based 3D reconstruction of the Priscilla catacombs in Rome, as carried out in the European ROVINA project. The 3D reconstruction system was mounted on a small mobile robot, which could autonomously roam the labyrinth of the catacombs’ corridors. The 3D reconstruction system was designed to cope with the specific challenges posed by the narrow passages found there. It consists of multiple cameras and light sources, mounted on spherical arcs. Also the structure-from-motion (SfM) software needed adaptation to optimally cope with the particular circumstances. Firstly, the information coming from the different cameras is handled jointly. Secondly, the feature matching needs to withstand the negative effects of the strongly changing illumination between different robot positions—moreover the environment is mostly dark and humid. Thirdly, for the same reasons, the usual texture mapping techniques would cause strong seams between the textures taken from different robot positions, and these were avoided through a more sophisticated analysis of surface reflectance characteristics. The chapter includes visual examples for parts of the 3D reconstruction.


Autonomous mapping 3D reconstruction Structure from motion Mobile robot 


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The authors gratefully acknowledge support by the EC FP7 project ROVINA.


  1. 1.
    V.A. Ziparo, G. Castelli, L. Van Gool, G. Grisetti, B. Leibe, M. Proesmans, C. Stachniss, The rovina project. Robots for exploration, digital preservation and visualization of archeological sites, in Proceedings of the 18th ICOMOS General Assembly and Scientific Symposium ‘Heritage and Landscape as Human Values’ (2014)Google Scholar
  2. 2.
    V.A. Ziparo, M. Zaratti, G. Grisetti, T.M. Bonanni, J. Serafin, M. Di Cicco, M. Proesmans, L. van Gool, O. Vysotska, I. Bogoslavskyi, C. Stachniss, Exploration and mapping of catacombs with mobile robots, in Safety, Security, and Rescue Robotics (SSRR) (2013)Google Scholar
  3. 3.
    A. Gruen, Digital close-range photogrammetry, in invited paper to G. Togliatti Memorial ‘Modern Trends in Photogrammetry’, XVII. ISPRS Congress, Washington, DC, August 1992Google Scholar
  4. 4.
  5. 5.
    T. Tuytelaars, L. van Gool, Matching widely separated views based on affinely invariant neighbourhoods. Int. J. Comput. Vis. 59(1), 61–85 (2004)CrossRefGoogle Scholar
  6. 6.
    R. Hartley, A. Zisserman, Multiple view geometry in computer vision (Cambridge University Press, New York, 2000)zbMATHGoogle Scholar
  7. 7.
    T. Moons, L. Van Gool, M. Vergauwen, 3D reconstruction from multiple images: Part 1 – principles. Found. Trends Comput. Graph. Vis. 4(4), 287–404 (2009)CrossRefGoogle Scholar
  8. 8.
    M. Pollefeys, R. Koch, L. Van Gool, Self-calibration and metric reconstruction inspite of varying and unknown intrinsic camera parameters. Int. J. Comput. Vis. 32(1), 7–25 (1999)CrossRefGoogle Scholar
  9. 9.
    M. Pollefeys, L. Van Gool, M. Vergauwen, F. Verbiest, K. Cornelis, J. Tops, R. Klein, Visual modeling with a hand-held camera. Int. J. Comput. Vis. 59(3), 207–232 (2004)CrossRefGoogle Scholar
  10. 10.
    P. Mueller, T. Vereenooghe, A. Ulmer, L. Van Gool, Automatic reconstruction of Roman housing architecture. Recording, Modeling and Visualization of Cultural Heritage (2005)Google Scholar
  11. 11.
    M. Vergauwen, L. Van Gool, Web-based 3D Reconstruction Service. Mach. Vis. Appl. 17(6), 411–426 (2006)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Agisoft Photoscan:
  14. 14.
    Microsoft Corporation. Kinect for XBOX 360Google Scholar
  15. 15.
    P. Vuylsteke, A. Oosterlinck, Single binary-encoded light pattern. IEEE Trans. Pattern Anal. Mach. Intell. 12(2), 148–164 (1990)CrossRefGoogle Scholar
  16. 16.
    M. Proesmans, L. Van Gool, A. Oosterlinck, One-shot active 3D shape acquisition, in IEEE International Conference on pattern Recognition (1996)Google Scholar
  17. 17.
    P. Cignoni, R. Scopigno, Sampled 3D models for CH applications: a viable and enabling new medium or just a technological exercise? J. Comput. Cult. Herit. 1(1), 1–23 (2008)CrossRefGoogle Scholar
  18. 18.
    H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, Speeded-up Robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  19. 19.
    R. Pless, Using many cameras as one, in Computer Vision and Pattern Recognition (2003)Google Scholar
  20. 20.
    M.D. Grossberg, S.K. Nayar, A general imaging model and a method for finding its parameters, in International Conference on Computer Vision (2001)Google Scholar
  21. 21.
    L. Hongdong, R. Hartley, K. Jae-Hak, A linear approach to motion estimation using generalized camera models, in Conference on Computer Vision and Pattern Recognition, (CVPR) (2008)Google Scholar
  22. 22.
    L. Kneip, H. Li, Efficient computation of relative pose for multi-camera systems, in Conference on Computer Vision and Pattern Recognition (CVPR) (2014)Google Scholar
  23. 23.
    J. Ventura, C. Arth, V. Lepetit, An efficient minimal solution for multi-camera motion, in International Conference on Computer Vision (ICCV) (2015)Google Scholar
  24. 24.
    G.H. Lee, F. Faundorfer, M. Pollefeys, Motion estimation for self-driving cars with a generalized camera, in Conference on Computer Vision and Pattern Recognition (CVPR) (2013)Google Scholar
  25. 25.
    M.A. Fischler, R.C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  26. 26.
    S. Georgoulis, M. Proesmans, L. Van Gool, Head-on analysis of shape and reflectance, in International Conference on 3D Vision (3DV), Tokyo, 8–11 Dec 2014Google Scholar
  27. 27.
    S. Georgoulis, V. Vanweddingen, M. Proesmans, L. Van Gool, A gaussian process latent variable model for brdf inference, in International conference on Computer Vision, ICCV (2015)Google Scholar
  28. 28.
    W. Matusik, H. Pfister, M. Brand, L. McMillan, A data-driven reflectance model, in SIGGRAPH (2003)Google Scholar
  29. 29.
    F. Romeiro, Y. Vasilyev, T. Zickler, Passive reflectometry, in European Conference on Computer Vision (ECCV) (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical Engineering-ESAT/PSIKU LeuvenLeuvenBelgium

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