Real-Time and Robust Monocular SLAM Using Predictive Multi-resolution Descriptors

  • Denis Chekhlov
  • Mark Pupilli
  • Walterio Mayol-Cuevas
  • Andrew Calway
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)


We describe a robust system for vision-based SLAM using a single camera which runs in real-time, typically around 30 fps. The key contribution is a novel utilisation of multi-resolution descriptors in a coherent top-down framework. The resulting system provides superior performance over previous methods in terms of robustness to erratic motion, camera shake, and the ability to recover from measurement loss. SLAM itself is implemented within an unscented Kalman filter framework based on a constant position motion model, which is also shown to provide further resilience to non-smooth camera motion. Results are presented illustrating successful SLAM operation for challenging hand-held camera movement within desktop environments.


Augmented Reality Scale Invariant Feature Transform Erratic Motion Search Region Unscented Kalman Filter 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: Proc. Int. Conf. on Computer Vision (2003)Google Scholar
  2. 2.
    Nistér, D.: Preemptive ransac for live structure and motion estimation. In: Proc. International Conference on Computer Vision (2003)Google Scholar
  3. 3.
    Chiuso, A., Favaro, P., Jin, H., Soatto, S.: Structure from motion causally integrated over time. IEEE Trans. on PAMI 24, 523–535 (2002)Google Scholar
  4. 4.
    Eade, E., Drummond, T.: Scalable monocular slam. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (2006)Google Scholar
  5. 5.
    Davison, A.J., Murray, D.W.: Mobile robot localisation using active vision. In: Proc. European Conference on Computer Vision (1998)Google Scholar
  6. 6.
    Dissanayake, M., Newman, P., Clark, S., Durrant-Whyte, H., Csorba, M.: A solution to the simultaneous localization and map building (slam) problem. IEEE Transactions on Robotics and Automation 17, 229–241 (2001)CrossRefGoogle Scholar
  7. 7.
    Pupilli, M., Calway, A.: Real-time visual slam with resilience to erratic motion. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (2006)Google Scholar
  8. 8.
    Molton, N., Ried, I., Davison, A.: Locally planar patch features for real-time structure from motion. In: Proc. British Machine Vision Conference (2004)Google Scholar
  9. 9.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. on PAMI 27, 1615–1630 (2005)Google Scholar
  11. 11.
    Gordon, I., Lowe, D.: Scene modelling, recognition and tracking with invariant image features. In: Int. Symp. on Mixed and Augmented Reality (2004)Google Scholar
  12. 12.
    Hutchings, R., Mayol, W.: Building recognition for mobile devices: incorporating positional information with visual features. Tech. Report, Univ. of Bristol (2005)Google Scholar
  13. 13.
    Wan, E., van der Merwe, R.: The unscented kalman filter. In: Haykin, S. (ed.) Kalman Filtering and Neural Networks. Wiley, Chichester (2001)Google Scholar
  14. 14.
    Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition, Seattle (1994)Google Scholar
  15. 15.
    Azarbayejani, A., Pentland, A.P.: Recursive estimation of motion, structure, and focal length. IEEE Trans on PAMI 17, 562–575 (1995)Google Scholar
  16. 16.
    Lemaire, T., Lacroix, S., Sol, J.: A practical 3d bearing only slam algorithm. In: IEEE International Conference on Intelligent Robots and Systems (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Denis Chekhlov
    • 1
  • Mark Pupilli
    • 1
  • Walterio Mayol-Cuevas
    • 1
  • Andrew Calway
    • 1
  1. 1.Department of Computer ScienceUniversity of BristolUK

Personalised recommendations