Skip to main content
SpringerLink
Log in

A comparative evaluation of interest point detectors and local descriptors for visual SLAM

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

In this paper we compare the behavior of different interest point detectors and descriptors under the conditions needed to be used as landmarks in vision-based simultaneous localization and mapping (SLAM). We evaluate the repeatability of the detectors, as well as the invariance and distinctiveness of the descriptors, under different perceptual conditions using sequences of images representing planar objects as well as 3D scenes. We believe that this information will be useful when selecting an appropriate landmark detector and descriptor for visual SLAM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Arras, K.O.: Feature-based robot navigation in known and unknown environments. Ph.D. thesis, Swiss Federal Institute of Technology Lausanne (EPFL), Thèse No. 2765 (2003)

  2. Bay, H., Tuytelaars, T., Gool, L.V.: SURF: Speeded up robust features. In: European Conference on Computer Vision (2006)

  3. Biber, P., Andreasson, H., Duckett, T., Schilling, A.: 3D modelling of indoor environments by a mobile robot with a laser scanner and panoramic camera. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2004)

  4. Davison A.J., Murray D.W.: Simultaneous localisation and map-building using active vision. IEEE Trans. Pattern Anal. Mach. Intell. 24, 865–880 (2002)

    Article  Google Scholar 

  5. Eustice, R., Singh, H., Leonard, J.: Exactly sparse delayed-state filters. In: IEEE International Conference on Robotics and Automation (2005)

  6. Fraundorfer, F., Bischof, H.: A novel performance evaluation method of local detectors on non-planar scenes. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) (2005)

  7. Gil, A., Reinoso, O., Burgard, W., Stachniss, C., Martínez Mozos, O.: Improving data association in rao-blackwellized visual SLAM. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2006)

  8. Grisetti G., Stachniss C., Burgard W.: Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE Trans. Robotics 23(1), 34–46 (2007)

    Article  Google Scholar 

  9. Hähnel, D., Burgard, W., Fox, D., Thrun, S.: An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas (2003)

  10. Harris, C.G., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference (1998)

  11. Jensfelt, P., Kragic, D., Folkesson, J., Björkman, M.: A framework for vision based bearing only 3D SLAM. In: IEEE International Conference on Robotics and Automation (2006)

  12. Kadir, T., Brady, M., Zisserman, A.: An affine invariant method for selecting salient regions in images. In: Proceedings of the 8th European Conference on Computer Vision, pp. 345–457 (2004)

  13. Kosecka, J., Zhou, L., Barber, P., Duric, Z.: Qualitative image based localization in indoor environments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2003)

  14. Little, J., Se, S., Lowe, D.: Global localization using distinctive visual features. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2002)

  15. Lowe, D.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision (1999)

  16. Lowe D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 2(60), 91–110 (2004)

    Article  Google Scholar 

  17. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the 13th British Machine Vision Conference, pp. 384–393 (2002)

  18. Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: International Conference on Computer Vision (2001)

  19. Mikolajczyk K., Schmid C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  20. Mikolajczyk K., Tuytelaars T., Schmid C., Zisserman A., Matas J., Schaffalitzky F., Kadir T., Van Gool L.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1/2), 43–72 (2005)

    Article  Google Scholar 

  21. Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: Fastslam: a factored solution to the simultaneous localization and mapping problem. In: Eighteenth national conference on Artificial Intelligence, pp. 593–598. American Association for Artificial Intelligence, Menlo Park (2002)

  22. Murillo, A.C., Guerrero, J.J., Sagüés, C.: Surf features for efficient robot localization with omnidirectional images. In: IEEE International Conference on Robotics and Automation (2007)

  23. Schmid, C., Mohr, R., Bauckhage, C. (2000). Evaluaton of interest point detectors. Int. J. Comput. Vis. 37(2)

  24. Se, S., Lowe, D.G., Little, J.: Vision-based mobile robot localization and mapping using scale-invariant features. In: IEEE International Conference on Robotics and Automation (2001)

  25. Sim, R., Elinas, P., Griffin, M., Little, J.: Vision-based slam using the rao-blackwellised particle filter. In: IJCAI Workshop on Reasoning with Uncertainty in Robotics (RUR) (2005)

  26. Smith, S.: A new class of corner finder. In: British Machine Vision Conference (1992)

  27. Theodoridis S., Koutroumbas K.: Pattern Recognition, 3rd edn. Academic Press, New York (2006)

    MATH  Google Scholar 

  28. Triebel, R., Burgard, W.: Improving simultaneous mapping and localization in 3D using global constraints. In: National Conference on Artificial Intelligence (AAAI) (2005)

  29. Valls Miro, J., Zhou, W., Dissanayake, G.: Towards vision based navigation in large indoor environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2006)

  30. Zernike F.: Diffraction theory of the cut procedure and its improved form, the phase contrast method. Physica 1, 689–704 (1934)

    Article  MATH  Google Scholar 

  31. Zhang Z., Deriche R., Faugeras O., Luong Q.: A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artif. Intell. 78, 87–119 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Systems Engineering, Miguel Hernández University, Elche, Spain

    Arturo Gil, Monica Ballesta & Oscar Reinoso

  2. Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany

    Oscar Martinez Mozos

Authors
  1. Arturo Gil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oscar Martinez Mozos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Monica Ballesta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Oscar Reinoso
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arturo Gil.

Additional information

This work has been supported by the EU under project CoSy FP6-004250-IPCoSy, by the Spanish Government under projects DPI2004-07433-C02-01 and CICYT DPI2007-61197 and by the Generalitat Valenciana under grant BFPI/2007/096.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gil, A., Mozos, O.M., Ballesta, M. et al. A comparative evaluation of interest point detectors and local descriptors for visual SLAM. Machine Vision and Applications 21, 905–920 (2010). https://doi.org/10.1007/s00138-009-0195-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-009-0195-x

Keywords

Search

Navigation