Autonomous Robots

, Volume 35, Issue 4, pp 255–269 | Cite as

Frequency-based underwater terrain segmentation

  • B. Douillard
  • N. Nourani-Vatani
  • M. Johnson-Roberson
  • O. Pizarro
  • S. Williams
  • C. Roman
  • I. Vaughn


A method for segmenting three-dimensional data of underwater unstructured terrains is presented. The three-dimensional point clouds are converted to two-dimensional elevation maps and analyzed for segmentation in the frequency domain. The lower frequency components represent the slower varying undulations of the underlying ground. The cut-off frequency, below which the frequency components form the ground surface, is determined automatically using peak detection. The user can also specify a maximum admissible size of objects to drive the automatic detection of the cut-off frequency. The points above the estimated ground surface are clustered via standard proximity clustering to form object segments. The precision of the segmentation is compared against ground truth hand labelled data acquired by a stereo camera pair and a structured light sensor. It is also evaluated for registration error when the extracted segments are used for sub-map alignment. The proposed approach is compared to three point cloud based and two image based segmentation algorithms. The results show that the approach is applicable to a range of different terrains and is able to generate features useful for navigation.


Perception Segmentation Underwater  Scan registration 3D processing Structured light  Dense stereo 



This research was supported by the Australian Research Council (ARC) through the Discovery program (DP110101986), the Australian Government through the SIEF program, and by the Australian Centre for Field Robotics at the University of Sydney. The authors would like to thank Alastair Quadros, Peter Morton and Vsevolod Vlaskine for valuable help with software, as well as James P. Underwood, Mitch Bryson and Donald Danserau for useful discussions.


  1. Besl, P. J., & McKay, H. D. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 14(2), 239–256.CrossRefGoogle Scholar
  2. Bogdan Rusu, R., & Cousins, S. (2011). 3D is here: Point cloud library (PCL). In IEEE international conference on Robotics and automation (ICRA). Shanghai, China (May 9–13, 2011).Google Scholar
  3. Borrmann, D., Elseberg, J., Lingemann, K., Nüchter, A., & Hertzberg, J. (2008). Globally consistent 3d mapping with scan matching. Robotics and Autonomous Systems, 56(2), 130–142.CrossRefGoogle Scholar
  4. Boykov, Y., & Funka-Lea, G. (2006). Graph cuts and efficient and image segmentation. International Journal of Computer Vision, 70(2), 109–131.CrossRefGoogle Scholar
  5. Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 25(11), 120.Google Scholar
  6. Brown, C. J., Smith, S. J., Lawton, P., & Anderson, J. T. (2011). Benthic habitat mapping: A review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques. Estuarine Coastal and Shelf Science, 92, 502–520.CrossRefGoogle Scholar
  7. Chen, X., Golovinskiy, A., & Funkhouser, T. (2009). A benchmark for 3d mesh segmentation. ACM Transactions on Graphics (TOG), 28(3), 73.CrossRefGoogle Scholar
  8. Douillard, B., Nourani-Vatani, N., Johnson-Roberson, M., Williams, S., Roman, C., Pizarro, O., Vaughn, I., & Inglis, G. (2012a). FFT-based terrain segmentation for underwater mapping. In Robotics: Science and Systems (RSS), Sydney, Australia.Google Scholar
  9. Douillard, B., Quadros, A., Morton, P., Underwood, J. P., Deuge, M. D., & Hugosson, S., et al. (2012b). Scan segments matching for pairwise 3D alignment. In IEEE International Conference on Robotics and Automation (ICRA).Google Scholar
  10. Douillard, B., Underwood, J., Kuntz, N., Vlaskine, V., Quadros, A., & Morton, P., et al. (2010). On the segmentation of 3D LIDAR point clouds. In IEEE International Conference on Robotics and Automation (ICRA).Google Scholar
  11. Felzenszwalb, P., & Huttenlocher, D. H. (2004). Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167–181.CrossRefGoogle Scholar
  12. Geiger, A., Roser, M., & Urtasun, R. (2010). Efficient large-scale stereo matching. In Asian Conference on Computer Vision. Queenstown, New Zealand (November 2010).Google Scholar
  13. Golovinskiy, A., & Funkhouser, T. (2009). Min-cut based segmentation of point clouds. In IEEE Internationa Conference on Computer Vision (ICCV) Workshops (pp. 39–46).Google Scholar
  14. Hirschmuller, H. (2008). Stereo processing by semiglobal matching and mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 328–341.CrossRefGoogle Scholar
  15. Horn, B. (1986). Robot vision. Cambridge: The MIT Press.Google Scholar
  16. Kalogerakis, E., Hertzmann, A., & Singh, K. (2010). Learning 3d mesh segmentation and labeling. ACM Transactions on Graphics (TOG), 29(4), 102.CrossRefGoogle Scholar
  17. Kolmogorov, V., & Zabih, R. (2001). Computing visual correspondence with occlusions using graph cuts. In Computer Vision, 2001. ICCV 2001. Proceedings 8th IEEE International Conference on (Vol. 2, pp. 508–515).Google Scholar
  18. Kunis, S. (2006). Nonequispaced FFT - Generalisation and Inversion. Ph.D. thesis, Germany: Universität Lübeck.Google Scholar
  19. Pauling, F., Bosse, M., & Zlot, R. (2009). Automatic segmentation of 3D laser point clouds by ellipsoidal region growing. In Australasian Conference on Robotics and Automation (ACRA).Google Scholar
  20. Petillot, Y., Ruiz, I., & Lane, D. (2001). Underwater vehicle obstacle avoidance and path planning using a multi-beam forward looking sonar. IEEE Journal of Oceanic Engineering, 26(2), 240–251.Google Scholar
  21. Proakis, J. G., & Manolakis, D. G. (1996). Digital signal processing—Principles, algorithms, and applications (3rd ed.). Englewood Cliffs: Prentice Hall International.Google Scholar
  22. Reed, S., Ruiz, I., Capus, C., & Petillot, Y. (2006). The fusion of large scale classified side-scan sonar image mosaics. IEEE Transactions on Image Processing, 15(7), 2049–2060.CrossRefGoogle Scholar
  23. Roman, C., Inglis, G., & Rutter, J. (2010). Application of structured light imaging for high resolution mapping of underwater archaeological sites. In IEEE OCEANS, Sydney (pp. 1–9).Google Scholar
  24. Roman, C., & Singh, H. (2006). Consistency based error evaluation for deep sea bathymetric mapping with robotic vehicles. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 3568–3574).Google Scholar
  25. Roman, C., & Singh, H. (2007). A self-consistent bathymetric mapping algorithm. Journal of Field Robotics, 24(1–2), 23–50.zbMATHCrossRefGoogle Scholar
  26. Salvi, J., Matabosch, C., Fofi, D., & Forest, J. (2007). A review of recent range image registration methods with accuracy evaluation. Image and Vision Computing, 25(5), 578–596.CrossRefGoogle Scholar
  27. Schoenberg, J., Nathan, A., & Campbell, M. (2010). Segmentation of dense range information in complex urban scenes. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google Scholar
  28. Seber, G. (1984). Multivariate observations (Vol. 41). New York: Wiley.zbMATHCrossRefGoogle Scholar
  29. Segal, A., Haehnel, D., & Thrun, S. (2009). Generalized-icp. In Proc. of Robotics: Science and Systems (RSS) (Vol. 25, pp. 26–27).Google Scholar
  30. Sheehan, M., Harrison, A., & Newman, P. (2010). Automatic self-calibration of a full field-of-view 3d n-laser scanner. In Proceedings of the International Symposium on Experimental, Robotics (pp. 1–14).Google Scholar
  31. Steingrube, P., Gehrig, S., & Franke, U. (2009). Performance evaluation of stereo algorithms for automotive applications. In Computer vision systems (pp. 285–294).Google Scholar
  32. Strom, J., Richardson, A., & Olson, E. (2010). Graph-based segmentation of colored 3d laser point clouds. In IEEE/RSJ International Conference on Intelligent Robots and Systems.Google Scholar
  33. Triebel, R., Shin, J., & Siegwart, R. (2010). Segmentation and unsupervised part-based discovery of repetitive objects. In Robotics: Science and Systems, Zaragoza, Spain.Google Scholar
  34. Williams, S., Pizarro, O., Jakuba, M., & Barrett, N. (2010). AUV benthic habitat mapping in South Eastern Tasmania. In Field and Service Robotics (pp. 275–284). Springer, Berlin.Google Scholar
  35. Yuan, J., Egil Bae, X. C. T., & Boykov, Y. (2010). A continuous max-flow approach to Potts model. Computer Vision–ECCV, 6316, 379–392.Google Scholar
  36. Zhu, X., Zhao, H., Liu, Y., Zhao, Y., & Zha, H. (2010). Segmentation and classification of range image from an intelligent vehicle in urban environment. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1457–1462).Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • B. Douillard
    • 1
  • N. Nourani-Vatani
    • 2
  • M. Johnson-Roberson
    • 3
  • O. Pizarro
    • 2
  • S. Williams
    • 2
  • C. Roman
    • 4
  • I. Vaughn
    • 4
  1. 1.Jet Propulsion LaboratoryPasadenaUSA
  2. 2.Australian Centre for Field RoboticsThe University of SydneySydneyAustralia
  3. 3.The Department of Naval Architecture and Marine EngineeringUniversity of MichiganAnn ArborUSA
  4. 4.Department of Ocean EngineeringThe University of Rhode IslandNarragansettUSA

Personalised recommendations