Camera-Agnostic Monocular SLAM and Semi-dense 3D Reconstruction

  • Martin RünzEmail author
  • Frank Neuhaus
  • Christian Winkens
  • Dietrich Paulus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)


This paper discusses localisation and mapping techniques based on a single camera. After introducing the given problem, which is known as monocular SLAM, a new camera agnostic monocular SLAM system (CAM-SLAM) is presented. It was developed within the scope of this work and is inspired by recently proposed SLAM-methods. In contrast to most other systems, it supports any central camera model such as for omnidirectional cameras. Experiments show that CAM-SLAM features similar accuracy as state-of-the-art methods, while being considerably more flexible.


Camera Model Epipolar Line Visual Odometry Omnidirectional Camera Pinhole Camera Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Martin Rünz has been partly supported by the SecondHands project, funded from the EU Horizon 2020 Research and Innovation programme under grant agreement No. 643950.


  1. 1.
    Baker, S., Nayar, S.K.: A theory of single-viewpoint catadioptric image formation. Int. J. Comput. Vis. 35(2), 175–196 (1999)CrossRefGoogle Scholar
  2. 2.
    Bunschoten, R., Krse, B.: Robust scene reconstruction from an omnidirectional vision system. IEEE Trans. Robot. Autom. 19(2), 351–357 (2003)CrossRefGoogle Scholar
  3. 3.
    Burbridge, C., Spacek, L., Condell, J., Nehmzow, U.: Monocular omnidirectional vision based robot localisation and mapping. In: Proceedings of the TAROS (2008)Google Scholar
  4. 4.
    Civera, J., Davison, A.J., Montiel, J.: Inverse depth parametrization for monocular slam. IEEE Trans. Robot. 24(5), 932–945 (2008)CrossRefGoogle Scholar
  5. 5.
    Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: Proceedings of Ninth IEEE International Conference on Computer Vision, pp. 1403–1410. IEEE (2003)Google Scholar
  6. 6.
    Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part II. LNCS, vol. 8690, pp. 834–849. Springer, Heidelberg (2014)Google Scholar
  7. 7.
    Gamallo, C., Mucientes, M., Regueiro, C.V.: A FastSLAM-based algorithm for omnidirectional cameras. J. Phys. Agents 7, 12–21 (2013)Google Scholar
  8. 8.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2012)Google Scholar
  9. 9.
    Geyer, C., Daniilidis, K.: Catadioptric camera calibration. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, pp. 398–404. IEEE (1999)Google Scholar
  10. 10.
    Gutierrez, D., Rituerto, A., Montiel, J.M.M., Guerrero, J.J.: Adapting a real-time monocular visual slam from conventional to omnidirectional cameras. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 343–350. IEEE (2011)Google Scholar
  11. 11.
    Hartley, R., Gupta, R., Chang, T.: Stereo from uncalibrated cameras. In: 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Proceedings CVPR 1992, pp. 761–764, June 1992Google Scholar
  12. 12.
    Horn, B.K., Hilden, H.M., Negahdaripour, S.: Closed-form solution of absolute orientation using orthonormal matrices. JOSA A, 5(7), 1127–1135 (1988)Google Scholar
  13. 13.
    Klein, G., Murray, D.: Parallel tracking and mapping on a camera phone. In: Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2009, Washington, DC, pp. 83–86. IEEE Computer Society (2009)Google Scholar
  14. 14.
    Labrosse, F.: The visual compass: performance and limitations of an appearance-based method. J. Field Robot. 23(10), 913–941 (2006)CrossRefGoogle Scholar
  15. 15.
    Lhuillier, M.: Effective and generic structure from motion using angular error. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 1, pp. 67–70 (2006)Google Scholar
  16. 16.
    Mei, C., Rives, P.: Single view point omnidirectional camera calibration from planar grids. In: 2007 IEEE International Conference on Robotics and Automation, pp. 3945–3950. IEEE (2007)Google Scholar
  17. 17.
    Mei, C., Sibley, G., Newman, P.: Closing loops without places. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3738–3744. IEEE (2010)Google Scholar
  18. 18.
    Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Application VISSAPP 2009, pp. 331–340. INSTICC Press (2009)Google Scholar
  19. 19.
    Mur-Artal, R., Montiel, J.M.M., Tardós, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. Submitted to IEEE Trans. Robot. (2015). arXiv preprint arXiv:1502.00956
  20. 20.
    Mur-Artal, R., Tards, J.D.: Probabilistic semi-dense mapping from highly accurate feature-based monocular slam. In Robotics: Science and Systems (2015)Google Scholar
  21. 21.
    Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: dense tracking and mapping in real-time. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2320–2327. IEEE (2011)Google Scholar
  22. 22.
    Peri, V., Nayar, S.K.: Generation of perspective and panoramic video from omnidirectional video. In: Proceedings of DARPA Image Understanding Workshop, vol. 1, pp. 243–245. Citeseer (1997)Google Scholar
  23. 23.
    Phan, K.D., Ovchinnikov, A.V.: Indoor slam using an omnidirectional camera. Middle East J. Sci. Res. 16(1), 88–94 (2013)Google Scholar
  24. 24.
    Ramer, U.: An iterative procedure for the polygonal approximation of plane curves. Comput. Graph. Image Process. 1(3), 244–256 (1972)CrossRefGoogle Scholar
  25. 25.
    Recker, S., Hess-Flores, M., Joy, K.I.: Statistical angular error-based triangulation for efficient and accurate multi-view scene reconstruction. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV), pp. 68–75. IEEE (2013)Google Scholar
  26. 26.
    Rituerto, A., Puig, L., Guerrero, J.J.: Visual slam with an omnidirectional camera. In: 20th International Conference on Pattern Recognition (ICPR), pp. 348–351. IEEE (2010)Google Scholar
  27. 27.
    Scaramuzza, D., Siegwart, R.: Appearance-guided monocular omnidirectional visual odometry for outdoor ground vehicles. IEEE Trans. Robot. 24(5), 1015–1026 (2008)CrossRefGoogle Scholar
  28. 28.
    Scaramuzza, D.: Omnidirectional vision: from calibration to robot motion estimation. PhD thesis. Citeseer (2008)Google Scholar
  29. 29.
    Scaramuzza, D., Martinelli, A., Siegwart, R.: A toolbox for easily calibrating omnidirectional cameras. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5695–5701. IEEE (2006)Google Scholar
  30. 30.
    Schoenbein, M., Geiger, A.: Omnidirectional 3D reconstruction in augmented manhattan worlds. In: International Conference on Intelligent Robots and Systems, pp. 716–723, Chicago. IEEE, October 2014Google Scholar
  31. 31.
    Schnbein, M., Strauss, T., Geiger, A.: Calibrating and centering quasi-central catadioptric cameras. In: International Conference on Robotics and Automation (ICRA) (2014)Google Scholar
  32. 32.
    Strasdat, H., Davison, A.J., Montiel, J.M.M., Konolige, K.: Double window optimisation for constant time visual slam. In: IEEE International Conference on Computer Vision (ICCV), pp. 2352–2359, November 2011Google Scholar
  33. 33.
    Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D slam systems. In: Proceedings of the International Conference on Intelligent Robot Systems (IROS), October 2012Google Scholar
  34. 34.
    Sturm, P.: Camera models and fundamental concepts used in geometric computer vision. Found. Trends Comput. Graph. Vis. 6(1–2), 1–183 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Martin Rünz
    • 1
    Email author
  • Frank Neuhaus
    • 1
  • Christian Winkens
    • 1
  • Dietrich Paulus
    • 1
  1. 1.University of Koblenz-LandauMainzGermany

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