Advertisement

BRISK-Based Visual Feature Extraction for Resource Constrained Robots

  • Daniel Jaymin Mankowitz
  • Subramanian Ramamoorthy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8371)

Abstract

We address the problem of devising vision-based feature extraction for the purpose of localisation on resource constrained robots that nonetheless require reasonably agile visual processing. We present modifications to a state-of-the-art Feature Extraction Algorithm (FEA) called Binary Robust Invariant Scalable Keypoints (BRISK) [8]. A key aspect of our contribution is the combined use of BRISK0 and U-BRISK as the FEA detector-descriptor pair for the purpose of localisation. We present a novel scoring function to find optimal parameters for this FEA. Also, we present two novel geometric matching constraints that serve to remove invalid interest point matches, which is key to keeping computations tractable. This work is evaluated using images captured on the Nao humanoid robot. In experiments, we show that the proposed procedure outperforms a previously implemented state-of-the-art vision-based FEA called 1D SURF (developed by the rUNSWift RoboCup SPL team), on the basis of accuracy and generalisation performance. Our experiments include data from indoor and outdoor environments, including a comparison to datasets such as based on Google Streetview.

Keywords

BRISK BRISK0 - U-BRISK feature extraction localisation resource constrained robot Nao Humanoid Robot 

References

  1. 1.
  2. 2.
    Anderson, P., Yusmanthia, Y., Hengst, B., Sowmya, A.: Robot Localisation Using Natural Landmarks. In: Chen, X., Stone, P., Sucar, L.E., van der Zant, T. (eds.) RoboCup 2012. LNCS (LNAI), vol. 7500, pp. 118–129. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Computer Vision and Image Understanding 110(3), 346–359 (2008)CrossRefGoogle Scholar
  4. 4.
    Briggs, A., Yunpeng, L., Scharstein, D.: Feature matching across 1D panoramas. In: Proc. IEEE Workshop on Omnidirectional Vision and Camera Networks (2005)Google Scholar
  5. 5.
    Juan, L., Gwun, O.: A comparison of sift, pca-sift and surf. International Journal of Image Processing (IJIP) 3(4), 143–152 (2009)Google Scholar
  6. 6.
    Lategahn, H., Geiger, A., Kitt, B.: Visual SLAM for autonomous ground vehicles. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1732–1737. IEEE (2011)Google Scholar
  7. 7.
    Leutenegger, S., Chli, M., Siegwart, R.: BRISK: Binary robust invariant scalable keypoints. In: Proc. International Conference on Computer Vision (ICCV), pp. 2548–2555. IEEE (2011)Google Scholar
  8. 8.
    Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and Generic Corner Detection Based on the Accelerated Segment Test. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 183–196. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Mankowitz, D.J.: BRISK-based Visual Landmark Localisation using Nao Humanoid Robots. MSc. Thesis, MSc. in Artificial Intelligence, University of Edinburgh (2012)Google Scholar
  10. 10.
    RoboCup, Standard Platform League (2012), http://www.tzi.de/spl/bin/view/Website/WebHome
  11. 11.
    Rosten, E., Drummond, T.W.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Se, S., Lowe, D., Little, J.: Vision-based Mobile Robot Localization and Mapping using Scale-Invariant Features. In: Proc. International Conference on Robotics and Automation (ICRA), pp. 2051–2058 (2001)Google Scholar
  13. 13.
    Thomas, S., Salvi, J., Petillot, Y.: Real-time Stereo Visual SLAM. MSc. Thesis, MSc. Erasmus Mundus in Vision and Robotics (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Daniel Jaymin Mankowitz
    • 1
  • Subramanian Ramamoorthy
    • 1
  1. 1.School of InformaticsUniversity of EdinburghEdinburghUK

Personalised recommendations