Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-18T23:51:47.426Z Has data issue: false hasContentIssue false
  • English
  • Français

Precise Bathymetry as a Step Towards Producing Bathymetric Electronic Navigational Charts for Comparative (Terrain Reference) Navigation

Published online by Cambridge University Press:  17 May 2019

Andrzej Stateczny ,
Daria Gronska-Sledz  and
Weronika Motyl
Andrzej Stateczny*
Affiliation:
(Department of Geodesy, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland)
Daria Gronska-Sledz
Affiliation:
(Marine Technology Ltd, Szczecin, Poland)
Weronika Motyl
Affiliation:
(Marine Technology Ltd, Szczecin, Poland)
*
(E-mail: andrzej.stateczny@pg.edu.pl)
Get access Rights & Permissions [Opens in a new window]

Abstract

Bathymetric Electronic Navigational Charts (bENCs) contain only bathymetry data and can be used in applications such as underwater positioning, dredging and piloting. According to International Hydrographic Organization (IHO) standard S-57, Electronic Navigational Charts (ENCs) contain depth information with pure density of depth contours. Typical depth contours encoded by Hydrographic Offices are limited to 2, 5, 10 and 20 m. Availability of more depth contours in bENCs would allow the visualisation of a safety contour which is closer to users' specific needs, especially in restricted waters such as ports, lakes and rivers. Another problem is non – Global Navigation Satellite System (GNSS) Unmanned Underwater Vehicle (UUV) navigation. bENCs could be used as reference data for UUV comparative navigation. This is called terrain reference navigation. This article presents the results from bathymetric data processing that was performed to convert data contained in bENCs into a reference for underwater comparative navigation. We use data obtained using a multibeam echo sounder to produce depth data with a horizontal spacing of 0·10 m that is suitable for use in restricted waters. The experimental data was collected in and around the Port of Gdansk, Poland.

Keywords

Electronic Navigational ChartBathymetry
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 6 , November 2019 , pp. 1623 - 1632
Copyright
Copyright © The Royal Institute of Navigation 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Canadian Hydrographic Service (2013). Standards for Hydrographic Surveys. June 2013, Edition 2. http://www.charts.gc.ca/data-gestion/standards-normes/intro-eng.aspGoogle Scholar
Chen, P., Li, Y., Su, Y., Chen, X. and Jiang, Y. (2015a). Review of AUV Underwater Terrain Matching Navigation. The Journal of Navigation, 68, 1155–1172.Google Scholar
Chen, P., Li, Y., Su, Y., Chen, X. and Jiang, Y. (2015b). Underwater terrain positioning method based on least squares estimation for AUV. China Ocean Engineering, 29, 859–874.Google Scholar
Claus, B. and Bachmayer, R. (2015). Terrain-aided Navigation for an Underwater Glider. Journal of Field Robotics, 32, 935–951.Google Scholar
Dong, M., Chou, W. and Fang, B. (2017). Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data. Scientific Programming, article 7136702.Google Scholar
Hagen, O., Anonsen, K. and Saebo, T. (2015). Toward Autonomous Mapping with AUVs - Line-to-Line Terrain Navigation. Oceans 2015 - MTS/IEEE, Washington DC.Google Scholar
Han, Y., Wang, B., Deng, Z. and Fu, M. (2016). An Improved TERCOM-Based Algorithm for Gravity-Aided Navigation. IEEE Sensors Journal, 16, 2537–2544.Google Scholar
Hyla, T., Wawrzyniak, N. and Kazimierski, W. (2015). Model of Collaborative Data Exchange for Inland Mobile Navigation. Proceedings of Soft Computing in Computer and Information Science Conference, Miedzyzdroje, Poland. Advances in Intelligent Systems and Computing, 342, 435–444.Google Scholar
Jung, J., Li, J., Choi, H. and Myung, H. (2017). Localization of AUVs using visual information of underwater structures and artificial landmarks. Intelligent Service Robotics, 10, 67–76.Google Scholar
Kazimierski, W. and Wlodarczyk-Sielicka, M. (2016). Technology of Spatial Data Geometrical Simplification in Maritime Mobile Information System for Coastal Waters. Polish Maritime Research, 23, 3–12.Google Scholar
Li, Y., Ma, T., Chen, P., Jiang, Y., Wang, R. and Zhang, Q. (2017a). Autonomous underwater vehicle optimal path planning method for seabed terrain matching navigation. Ocean Engineering, 133, 107–115.Google Scholar
Li, Y., Ma, T., Wang, R., Chen, P., Shen, P. and Jiang, Y. (2017b). Terrain Matching Positioning Method Based on Node Multi-information Fusion. The Journal of Navigation, 70, 82–100.Google Scholar
Li, Y., Wang, R., Chen, P. and Zhang, Q. (2017c). Terrain Correlation Correction Method for AUV Seabed Terrain Mapping. The Journal of Navigation, 70, 1062–1078.Google Scholar
Lubczonek, J. (2016). Geoprocessing of High Resolution Imageries for Shoreline Extraction in the Process of the Production of Inland Electronic Navigational Charts. Photogrammetrie Fernerkundung Geoinformation, 4, 225–235.Google Scholar
Lubczonek, J. and Borawski, M. (2016). A New Approach to Geodata Storage and Processing Based on Neural Model of the Bathymetric Surface. Baltic Geodetic Congress (BGC Geomatics), 1–7.Google Scholar
Maleika, W. (2015a). Moving Average Optimization in Digital Terrain Model Generation Based on Test Multibeam Echosounder Data. Geo-Marine Letters, 35, 61–68.Google Scholar
Maleika, W. (2015b). The Influence of the Grid Resolution on the Accuracy of the Digital Terrain Model Used in Seabed Modelling. Marine Geophysical Research, 36, 35–44.Google Scholar
Menozzi, A., Hansen, J., Peele, G., Snarski, S. and Merrick, C. (2015). On the Development of a Framework for Underwater Localization Using All-Source Data. OCEANS 2015, Genova, Italy.Google Scholar
Quintas, J., Teixeira, F. and Pascoal, A. (2016). Magnetic Signal Processing Methods with Application to Geophysical Navigation of Marine Robotic Vehicles. OCEANS 2016 MTS/IEEE, Monterey, California.Google Scholar
Ramesh, R., Jyothi, V., Vedachalam, N., Ramadass, G. A. and Atmanand, M. A. (2016). Development and Performance Validation of a Navigation System for an Underwater Vehicle. The Journal of Navigation, 69, 1097–1113.Google Scholar
Salavasidis, G., Harris, C., McPhail, S., Phillips, A. B. and Rogers E. (2016). Terrain Aided Navigation for Long Range AUV Operations at Arctic Latitudes. 2016 IEEE/OES (AUV), Book Series: IEEE OES Autonomous Underwater Vehicles, 115–123.Google Scholar
Song, Z., Bian, H. and Zielinski, A. (2016). Application of acoustic image processing in underwater terrain aided navigation. Ocean Engineering, 121, 279–290.Google Scholar
Specht, C., Weintrit, A. and Specht, M. (2016). Determination of the Territorial Sea Baseline - Aspect of Using Unmanned Hydrographic Vessels. Transnav-International Journal on Marine Navigation and Safety of Sea Transportation, 10, 649–654.Google Scholar
Specht, C., Switalski, E. and Specht, M. (2017). Application of an Autonomous/Unmanned Survey Vessel (ASV/USV) in Bathymetric Measurements. Polish Maritime Research, 24, 36–44.Google Scholar
Stuntz, A., Kelly, J. and Smith, R. (2016). Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation. Frontiers in Robotics and AI, 3, article 23.Google Scholar
Wang, L., Yu, L. and Zhu, Y. (2015). Construction Method of the Topographical Features Model for Underwater Terrain Navigation. Polish Maritime Research, 22, 121–125.Google Scholar
Wawrzyniak, N. and Stateczny, A. (2017). MSIS Image Positioning in Port Areas with the Aid of Comparative Navigation Methods. Polish Maritime Research, 24, 32–41.Google Scholar
Wei, F., Yuan, Z. and Zhe, R. (2015). UKF-Based Underwater Terrain Matching Algorithms Combination. ACSR-Advances in Computer Science Research, 1027–1030.Google Scholar
Wlodarczyk-Sielicka, M., Lubczonek, J. and Stateczny, A. (2016). Comparison of Selected Clustering Algorithms of Raw Data Obtained by Interferometric Methods Using Artificial Neural Networks. Proceedings of the 16th International Radar Symposium (IRS), Krakow, Poland.Google Scholar
Wlodarczyk-Sielicka, M. and Stateczny, A. (2015). Selection of SOM Parameters for the Needs of Clusterisation of Data Obtained by Interferometric Methods. In Proceedings of the 16th International Radar Symposium (IRS), Dresden, Germany, 1129–1134.Google Scholar
Wu, L., Wang, H., Chai, H., Hsu, H. and Wang, Y. (2015). Research on the Relative Positions-Constrained Pattern Matching Method for Underwater Gravity-Aided Inertial Navigation. The Journal of Navigation, 68, 937–950.Google Scholar
Wu, M. and Yao, J. (2015). Adaptive UKF-SLAM Based on Magnetic Gradient Inversion Method for Underwater Navigation. Lectures Notes in Artificial Intelligence, 9245, 237–247.Google Scholar
Zhang, T., Xu, X. and Xu, S. (2015). Method of establishing an underwater digital elevation terrain based on kriging interpolation. Measurement, 63, 287–298.Google Scholar
Zhou, L., Cheng, X., Zhu, Y. and Lu, Y. (2015). Terrain Aided Navigation for Long-Range AUVs Using a New Bathymetric Contour Matching Method. IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Busan, South Korea.Google Scholar
Zhou, L., Cheng, X., Zhu, Y., Dai, C. and Fu, J. (2017). An Effective Terrain Aided Navigation for Low-Cost Autonomous Underwater Vehicles. Sensors, 17, article 680.Google Scholar
Zhou, L., Cheng, X. and Zhu, Y. (2016). Terrain aided navigation for autonomous underwater vehicles with coarse maps. Measurement Science and Technology, 27, article 095002.Google Scholar
Zhu, Z., Guo, Y. and Yang, Z. (2016). Study on Initial Gravity Map Matching Technique Based on Triangle Constraint Model. The Journal of Navigation, 69, 353–372.Google Scholar