Rotation Estimation for Two-Dimensional Forward-Looking Sonar Mosaicing

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 252)

Abstract

Two-dimensional forward-looking sonars are becoming standard sensors in both remotely operated and autonomous underwater vehicles, increasing the possibility of mapping under low visibility conditions. Due to the inherent nature of sonar image formation, the ideal mapping strategy relies on maintaining the same orientation, so as to minimize intensity alterations due to viewpoint changes. However, this is not always possible and therefore it is necessary to deal with the registration of sonar images under rotational movements. Previous investigations have discouraged the use of feature-based techniques and have suggested the use of global methods that are robust to noise, low-resolution and inhomogeneous insonification and can deal with the decoupled estimation of roto-translations. In this paper we review several candidate methods and assess them by using real data gathered under different conditions. By identifying the best approach for rotation estimation we aim to extend the applicability of sonar mosaicing to more diverse scenarios. Results indicate that applying phase correlation directly to polar frames leads to the highest accuracy under most cases.

Keywords

forward-looking sonar mosaicing rotation estimation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Walter, M.R.: Sparse bayesian information filters for localization and mapping. Ph.D. dissertation, Massachusetts Institute of Technology, Cambridge, MA (2008)Google Scholar
  2. 2.
    Hurtos, N., Cufi, X., Petillot, Y., Salvi, J.: Evaluation of registration methods on 2D Forward-Looking Sonar Imagery. In: IEEE/MTS OCEANS, Bergen (2013)Google Scholar
  3. 3.
    Johannsson, H., Kaess, M., Englot, B., Hover, F., Leonard, J.: Imaging sonar-aided navigation for autonomous underwater harbor surveillance. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4396–4403 (2010)Google Scholar
  4. 4.
    Negahdaripour, S., Firoozfam, P., Sabzmeydani, P.: On processing and registration of forward-scan acoustic video imagery. In: 2nd Canadian Conference Computer and Robot Vision, pp. 452–459 (2005)Google Scholar
  5. 5.
    Aykin, M., Negahdaripour, S.: On feature extraction and region matching for forward scan sonar imaging. In: IEEE OCEANS Hampton Road, pp. 1–9 (2012)Google Scholar
  6. 6.
    Negahdaripour, S.: On 3-D scene interpretation from F-S sonar imagery. In: IEEE OCEANS Hampton Road, pp. 1–9 (2012)Google Scholar
  7. 7.
    Hurtos, N., Cufi, X., Petillot, Y., Salvi, J.: Fourier-based registrations for two-dimensional forward-looking sonar image mosaicing. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5298–5305 (2012)Google Scholar
  8. 8.
    Chen, Q.S., Defrise, M., Deconinck, F.: Symmetric phase-only matched filtering of Fourier-Mellin transforms for image registration and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(12), 1156–1168 (1994)CrossRefGoogle Scholar
  9. 9.
    Reddy, B.S., Chatterji, B.N.: An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Transactions on Image Processing 5(8), 1266–1271 (1996)CrossRefGoogle Scholar
  10. 10.
    Tzimiropoulos, G., Argyriou, V., Zafeiriou, S., Stathaki, T.: Robust FFT-based scale-invariant image registration with image gradients. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(10), 1899–1906 (2010)CrossRefGoogle Scholar
  11. 11.
    Lucchese, L., Cortelazzo, G.M.: A noise-robust frequency domain technique for estimating planar roto-translations. IEEE Transactions on Signal Processing 48(6), 1769–1786 (2000)CrossRefGoogle Scholar
  12. 12.
    Keller, Y., Shkolnisky, Y., Averbuch, A.: The angular difference function and its application to image registration. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 969–976 (2005)CrossRefGoogle Scholar
  13. 13.
    Li, L., Qu, Z., Zeng, Q., Meng, F.: A novel approach to image roto-translation estimation. In: IEEE Int. Conf. on Automation and Logistics, pp. 2612–2616 (2007)Google Scholar
  14. 14.
    Costello, C.: Multi-reference frame image registration for rotation,translation, and scale. Technical Report, DTIC Document (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computer Vision and Robotics Group (ViCOROB)University of GironaGironaSpain

Personalised recommendations