An Evaluation of Image Feature Detectors and Descriptors for Robot Navigation

  • Adam Schmidt
  • Marek Kraft
  • Andrzej Kasiński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6375)


The detection and matching of feature points is crucial in many computer vision systems. Successful establishing of points correspondences between concurrent frames is important in such tasks as visual odometry, structure from motion or simultaneous localization and mapping. This paper compares of the performance of the well established, single scale detectors and descriptors and the increasingly popular, multi-scale approaches.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Nistér, D., Naroditsky, O., Bergen, J.: Visual odometry. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 652–659 (2004)Google Scholar
  2. 2.
    Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: MonoSLAM: Real-Time Single Camera SLAM. IEEE Transactions on PAMI 29(6), 1052–1067 (2007)Google Scholar
  3. 3.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  4. 4.
    Shi, J., Tomasi, C.: Good Features to Track. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 593–600 (1994)Google Scholar
  5. 5.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. Computer Vision and Image Understanding 110(3), 346–359 (2008)CrossRefGoogle Scholar
  7. 7.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Agrawal, M., Konolige, K., Blas, M.R.: CenSurE: Center surround extremas for realtime feature detection and matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 102–115. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Banks, J., Corke, P.: Quantitative Evaluation of Matching Methods and Validity Measures for Stereo Vision. The Int. J. of Robotics Research 20(7), 512–532 (2001)CrossRefGoogle Scholar
  10. 10.
    Lienhart, R., Maydt, J.: An Extended Set of Haar-like Features for Rapid Object Detection. In: Proc. of Int. Conf. on Image Processing, vol. 1, pp. 900–903 (2002)Google Scholar
  11. 11.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn., vol. 24, pp. 381–395. Cambridge University Press, Cambridge (2004)zbMATHGoogle Scholar
  12. 12.
    Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Adam Schmidt
    • 1
  • Marek Kraft
    • 1
  • Andrzej Kasiński
    • 1
  1. 1.Institute of Control and Information EngineeringPoznań University of TechnologyPoznańPoland

Personalised recommendations