Journal of Marine Science and Application

, Volume 18, Issue 3, pp 334–342 | Cite as

Fast Extraction of Local Underwater Terrain Features for Underwater Terrain-Aided Navigation

  • Pengyun ChenEmail author
  • Pengfei Zhang
  • Jianlong Chang
  • Peng Shen
Research Article


Terrain matching accuracy and real-time performance are affected by local underwater terrain features and structure of matching surface. To solve the extraction problem of local terrain features for underwater terrain-aided navigation (UTAN), real-time data model and selection method of beams are proposed. Then, an improved structure of terrain storage is constructed, and a fast interpolation strategy based on index is proposed, which can greatly improve the terrain interpolation–reconstruction speed. Finally, for the influences of tide, an elimination method of reference depth deviation is proposed, which can reduce the reference depth errors caused by tidal changes. As the simulation test shows, the proposed method can meet the requirements of real-time performance and effectiveness. Furthermore, the extraction time is considerably reduced, which makes the method suitable for the extraction of local terrain features for UTAN.


Underwater terrain modeling Beam selection Mixing resolution Terrain storage model Index extraction 



This study is supported by the National Natural Science Foundation of China (Grant No. 51775518), Natural Science Foundation of North University of China (Grant No. 2017001), and the 333 Academic Start Funding for Talents of North University of China (Grant No. 13011915).


  1. Anonsen KB, Hagen OK (2010) An analysis of real-time terrain aided navigation results from HUGIN AUV. Oceans 2010, Seattle, 1–9.
  2. Bergman N, Ljung L (1997) Point-mass filter and Cramer-Rao bound for terrain-aided navigation. IEEE Conference on Decision and Control, San Diego, 565–570.
  3. Carlstrom J, Nygren I (2005). Terrain navigation of the Swedish AUV62f vehicle. 14th International Symposium on Unmanned Untethered Submersible Technology, Durham, 1–10.
  4. Carreno S, Wilson P, Ridao P, Petillot Y (2010) A survey on terrain based navigation for AUVs. Oceans 2010, Seattle, 1–7.
  5. Chen PY, Li Y, Su YM, Chen XL, Jiang YQ (2015) Underwater terrain positioning method based on least squares estimation for AUV. China Ocean Eng 29(6):859–874. CrossRefGoogle Scholar
  6. Eleftherakis D, Snellen M, Amiri-Simkooei A, Simons DG, Siemes K (2014) Observations regarding coarse sediment classification based on multi-beam echo-sounder’s backscatter strength and depth residuals in Dutch rivers. J Acoust Soc Am 135(6):3305–3315. CrossRefGoogle Scholar
  7. Gao JY, Jin XL, Wu ZY (2003) Construction of submarine DTM from raw multibeam data. Mar Sci Bull 22(1):30–38 (in Chinese). Google Scholar
  8. GeoAcoustics Limited (2007) GeoSwath Plus operation manual. GeoAcoustics Limited, London, 1–25Google Scholar
  9. Hagen OK, Anonsen KB (2014) Using terrain navigation to improve marine vessel navigation systems. Mar Technol Soc J 48(2):45–58. CrossRefGoogle Scholar
  10. Lu YQ (2003) Study of the real-time rendering for large-scale terrain dataset. PhD thesis, Zhejiang University, Hangzhou, 1–6 (in Chinese)Google Scholar
  11. Meduna DK, Rock SM, McEwen RS (2010) Closed-loop terrain relative navigation for AUVs with non-inertial grade navigation sensors. Autonomous Underwater Vehicles (AUV), 2010 IEEE/OES, Monterey, 1-8.
  12. Nygren I, Jansson M (2004) Terrain navigation for underwater vehicles using the correlator method. IEEE J Ocean Eng 29(3):906–915. CrossRefGoogle Scholar
  13. Peng DD, Zhou T, Li HS, Zhang WY (2016) Terrain-aided navigation for underwater vehicles using maximum likelihood method. 2016 IEEE/OES China Ocean Acoustics Symposium, Harbin, 1–6.
  14. Su YM, Zhao JX, Cao J, Zhang G (2013) Dynamics modeling and simulation of autonomous underwater vehicles with appendages. J Mar Sci Appl 12(1):45–51. CrossRefGoogle Scholar
  15. Sun L, Liu YC, Li MS (2009) Technique on building DEM of bathymetry using multibeam data. Hydrographic Surveying and Charting 29(1):39–41 (in Chinese). Google Scholar
  16. Tan B, Xu Q, Ma DY (2003) Real-time multi-resolution terrain rendering using restricted quadtree. Journal of Computer-aided Design & Computer Craphics 15(3):270–276 (in Chinese). Google Scholar
  17. Tian FM (2007) Research on prior map data processing based terrain-aided navigation methods for underwater vehicles. Master thesis, Harbin Engineering University, Harbin, 10–20 (in Chinese)Google Scholar
  18. Wang YK, Zhu YL (2012) Terrain three-dimensional visualization based on dynamic LOD quadtree arithmetic. IEEE International Conference on Intelligent Control, Automatic Detection and High-End Equipment, Beijing, 1–5.
  19. Zhu Q, Li DR (1998) Error analysis and processing of multibeam soundings. Journal of Wuhan Technical University of Surveying and Mapping 23(1):1–4 (in Chinese). Google Scholar

Copyright information

© Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Pengyun Chen
    • 1
    Email author
  • Pengfei Zhang
    • 1
  • Jianlong Chang
    • 1
  • Peng Shen
    • 2
  1. 1.College of Mechatronic EngineeringNorth University of ChinaTaiyuanChina
  2. 2.National Deep Sea CenterQingdaoChina

Personalised recommendations