Advertisement

RT-SLAM: A Generic and Real-Time Visual SLAM Implementation

  • Cyril Roussillon
  • Aurélien Gonzalez
  • Joan Solà
  • Jean-Marie Codol
  • Nicolas Mansard
  • Simon Lacroix
  • Michel Devy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6962)

Abstract

This article presents a new open-source C++ implementation to solve the SLAM problem, which is focused on genericity, versatility and high execution speed. It is based on an original object oriented architecture, that allows the combination of numerous sensors and landmark types, and the integration of various approaches proposed in the literature. The system capacities are illustrated by the presentation of an inertial/vision SLAM approach, for which several improvements over existing methods have been introduced, and that copes with very high dynamic motions. Results with a hand-held camera are presented.

Keywords

Extend Kalman Filter Inertial Measurement Unit Intelligent Robot Proprioceptive Sensor Catadioptric Camera 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bailey, T., Nieto, J., Guivant, J., Stevens, M., Nebot, E.: Consistency of the ekf-slam algorithm. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3562–3568 (October 2006)Google Scholar
  2. 2.
    Civera, J., Davison, A.J., Montiel, J.M.M.: Inverse Depth to Depth Conversion for Monocular SLAM. In: Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp. 2778–2783 (April 2007)Google Scholar
  3. 3.
    Civera, J., Grasa, O.G., Davison, A.J., Montiel, J.M.M.: 1-point ransac for ekf-based structure from motion. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3498–3504 (October 2009)Google Scholar
  4. 4.
    Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: Proc. of IEEE International Conference on Computer Vision (ICCV), pp. 1403–1410 (October 2003)Google Scholar
  5. 5.
    Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: Monoslam: Real-time single camera slam. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1052–1067 (2007)CrossRefGoogle Scholar
  6. 6.
    Estrada, C., Neira, J., Tardos, J.D.: Hierarchical slam: Real-time accurate mapping of large environments. IEEE Trans. on Robotics 21(4), 588–596 (2005)CrossRefGoogle Scholar
  7. 7.
    Kwok, N.M., Dissanayake, G.: An efficient multiple hypothesis filter for bearing-only slam. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 1, pp. 736–741 (2004)Google Scholar
  8. 8.
    Lemaire, T., Lacroix, S., Sola, J.: A practical 3d bearing-only slam algorithm. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2449–2454 (August 2005)Google Scholar
  9. 9.
    Montiel, J.M.M.: Unified inverse depth parametrization for monocular slam. In: Proc. of Robotics: Science and Systems (RSS), pp. 16–19 (2006)Google Scholar
  10. 10.
    Sola, J.: Consistency of the monocular ekf-slam algorithm for three different landmark parametrizations. In: Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp. 3513–3518 (May 2010)Google Scholar
  11. 11.
    Sola, J., Monin, A., Devy, M.: Bicamslam: Two times mono is more than stereo. In: Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp. 4795–4800 (2007)Google Scholar
  12. 12.
    Sola, J., Monin, A., Devy, M., Lemaire, T.: Undelayed initialization in bearing only slam. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2499–2504 (2005)Google Scholar
  13. 13.
    Di Stefano, L., Mattoccia, S., Tombari, F.: Zncc-based template matching using bounded partial correlation. Pattern Recognition Letters 26(14), 2129–2134 (2005)CrossRefGoogle Scholar
  14. 14.
    Strasdat, H., Montiel, J.M.M., Davison, A.J.: Real-time monocular slam: Why filter? In: Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp. 2657–2664 (May 2010)Google Scholar
  15. 15.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 511–518 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cyril Roussillon
    • 1
    • 2
    • 3
  • Aurélien Gonzalez
    • 1
    • 2
  • Joan Solà
    • 1
    • 2
    • 4
  • Jean-Marie Codol
    • 1
    • 2
    • 5
  • Nicolas Mansard
    • 1
    • 2
  • Simon Lacroix
    • 1
    • 2
  • Michel Devy
    • 1
    • 2
  1. 1.CNRS, LAASToulouseFrance
  2. 2.UPS, INSA, INP, ISAE, LAASUniversité de ToulouseToulouseFrance
  3. 3.Funded by the Direction Générale de l’Armement (DGA)France
  4. 4.Ictineu SubmarinsBarcelonaSpain
  5. 5.NAV ON TIMEToulouseFrance

Personalised recommendations