{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:41:45Z","timestamp":1760488905378,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T00:00:00Z","timestamp":1569456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005632","name":"Narodowe Centrum Bada\u0144 i Rozwoju","doi-asserted-by":"publisher","award":["POIR.01.02.00-00-0074\/16"],"award-info":[{"award-number":["POIR.01.02.00-00-0074\/16"]}],"id":[{"id":"10.13039\/501100005632","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Autonomous navigation is an important task for unmanned vehicles operating both on the surface and underwater. A sophisticated solution for autonomous non-global navigational satellite system navigation is comparative (terrain reference) navigation. We present a method for fast processing of 3D multibeam sonar data to make depth area comparable with depth areas from bathymetric electronic navigational charts as source maps during comparative navigation. Recording the bottom of a channel, river, or lake with a 3D multibeam sonar data produces a large number of measuring points. A big dataset from 3D multibeam sonar is reduced in steps in almost real time. Usually, the whole data set from the results of a multibeam echo sounder results are processed. In this work, new methodology for processing of 3D multibeam sonar big data is proposed. This new method is based on the stepwise processing of the dataset with 3D models and isoline maps generation. For faster products generation we used the optimum dataset method which has been modified for the purposes of bathymetric data processing. The approach enables detailed examination of the bottom of bodies of water and makes it possible to capture major changes. In addition, the method can detect objects on the bottom, which should be eliminated during the construction of the 3D model. We create and combine partial 3D models based on reduced sets to inspect the bottom of water reservoirs in detail. Analyses were conducted for original and reduced datasets. For both cases, 3D models were generated in variants with and without overlays between them. Tests show, that models generated from reduced dataset are more useful, due to the fact, that there are significant elements of the measured area that become much more visible, and they can be used in comparative navigation. In fragmentary processing of the data, the aspect of present or lack of the overlay between generated models did not relevantly influence the accuracy of its height, however, the time of models generation was shorter for variants without overlay.<\/jats:p>","DOI":"10.3390\/rs11192245","type":"journal-article","created":{"date-parts":[[2019,9,27]],"date-time":"2019-09-27T03:03:15Z","timestamp":1569553395000},"page":"2245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4671-6827","authenticated-orcid":false,"given":"Andrzej","family":"Stateczny","sequence":"first","affiliation":[{"name":"Department of Geodesy, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Narutowicza 11-12, 80-233 Gdansk, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6169-1579","authenticated-orcid":false,"given":"Wioleta","family":"B\u0142aszczak-B\u0105k","sequence":"additional","affiliation":[{"name":"Institute of Geodesy, Faculty of Geodesy, Geospatial and Civil Engineering, University of Warmia and Mazury in Olsztyn, Oczapowskiego 2, 10-719 Olsztyn, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1668-0632","authenticated-orcid":false,"given":"Anna","family":"Sobieraj-\u017b\u0142obi\u0144ska","sequence":"additional","affiliation":[{"name":"Department of Geodesy, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Narutowicza 11-12, 80-233 Gdansk, Poland"}]},{"given":"Weronika","family":"Motyl","sequence":"additional","affiliation":[{"name":"Marine Technology Ltd., Roszczynialskiego 4\/6, 81-521 Gdynia, Poland"}]},{"given":"Marta","family":"Wisniewska","sequence":"additional","affiliation":[{"name":"Marine Technology Ltd., Roszczynialskiego 4\/6, 81-521 Gdynia, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1017\/S0373463315000429","article-title":"Review of AUV Underwater Terrain Matching Navigation","volume":"68","author":"Chen","year":"2015","journal-title":"J. Navig."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s13344-015-0060-9","article-title":"Underwater terrain positioning method based on least squares estimation for AUV","volume":"29","author":"Chen","year":"2015","journal-title":"China Ocean Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1002\/rob.21563","article-title":"Terrain-aided Navigation for an Underwater Glider","volume":"32","author":"Claus","year":"2015","journal-title":"J. Field Robot."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hagen, O., Anonsen, K., and Saebo, T. (2015, January 19\u201322). Toward Autonomous Mapping with AUVs\u2014Line-to-Line Terrain Navigation. Proceedings of the Oceans 2015-MTS\/IEEE Washington, Washington, DC, USA.","DOI":"10.23919\/OCEANS.2015.7404387"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s11370-016-0210-9","article-title":"Localization of AUVs using visual information of underwater structures and artificial landmarks","volume":"10","author":"Jung","year":"2017","journal-title":"Intell. Serv. Robot."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Salavasidis, G., Harris, C., McPhail, S., Phillips, A.B., and Rogers, E. (2016, January 6\u20139). Terrain Aided Navigation for Long Range AUV Operations at Arctic Latitudes. Proceedings of the 2016 IEEE\/OES Autonomous Underwater Vehicles (AUV), Tokyo, Japan.","DOI":"10.1109\/AUV.2016.7778658"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1017\/S037346331700011X","article-title":"Terrain Correlation Correction Method for AUV Seabed Terrain Mapping","volume":"70","author":"Li","year":"2017","journal-title":"J. Navig."},{"key":"ref_8","first-page":"7136702","article-title":"Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data","volume":"2017","author":"Dong","year":"2017","journal-title":"Sci. Program."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.oceaneng.2016.05.039","article-title":"Application of acoustic image processing in underwater terrain aided navigation","volume":"121","author":"Song","year":"2016","journal-title":"Ocean Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1017\/S0373463315001058","article-title":"Development and Performance Validation of a Navigation System for an Underwater Vehicle","volume":"69","author":"Ramesh","year":"2016","journal-title":"J. Navig."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.oceaneng.2017.01.026","article-title":"Autonomous underwater vehicle optimal path planning method for seabed terrain matching navigation","volume":"133","author":"Li","year":"2017","journal-title":"Ocean Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1017\/S0373463316000369","article-title":"Terrain Matching Positioning Method Based on Node Multi-information Fusion","volume":"70","author":"Li","year":"2017","journal-title":"J. Navig."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3389\/frobt.2016.00023","article-title":"Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation","volume":"3","author":"Stuntz","year":"2016","journal-title":"Front. Robot. AI"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1515\/pomr-2015-0043","article-title":"Construction Method of the Topographical Features Model for Underwater Terrain Navigation","volume":"22","author":"Wang","year":"2015","journal-title":"Pol. Marit. Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wei, F., Yuan, Z., and Zhe, R. (2015, January 10\u201311). UKF-Based Underwater Terrain Matching Algorithms Combination. Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference, Xi\u2019an, China.","DOI":"10.2991\/iiicec-15.2015.229"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"095002","DOI":"10.1088\/0957-0233\/27\/9\/095002","article-title":"Terrain aided navigation for autonomous underwater vehicles with coarse maps","volume":"27","author":"Zhou","year":"2016","journal-title":"Meas. Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.3390\/s17040680"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhou, L., Cheng, X., Zhu, Y., and Lu, Y. (2015, January 7\u201311). Terrain Aided Navigation for Long-Range AUVs Using a New Bathymetric Contour Matching Method. Proceedings of the 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Busan, Korea.","DOI":"10.1109\/AIM.2015.7222540"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1029\/2002GC000486","article-title":"Automatic processing of high-rate, high-density multibeam echosounder data","volume":"4","author":"Calder","year":"2003","journal-title":"Geochem. Geophys. Geosyst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kulawiak, M., and Lubniewski, Z. (2016, January 2\u20134). Processing of LiDAR and multibeam sonar point cloud data for 3D surface and object shape reconstruction. Proceedings of the 2016 Baltic Geodetic Congress (BGC Geomatics), Gda\u0144sk, Poland.","DOI":"10.1109\/BGC.Geomatics.2016.41"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s00367-014-0389-8","article-title":"Moving Average Optimization in Digital Terrain Model Generation Based on Test Multibeam Echosounder Data","volume":"35","author":"Maleika","year":"2015","journal-title":"Geo Mar. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s11001-014-9236-6","article-title":"The Influence of the Grid Resolution on the Accuracy of the Digital Terrain Model Used in Seabed Modelling","volume":"36","author":"Maleika","year":"2015","journal-title":"Mar. Geophys. Res."},{"key":"ref_23","first-page":"338","article-title":"Interpolating Bathymetric Big Data for an Inland Mobile Navigation System","volume":"47","year":"2018","journal-title":"Inf. Technol. Control."},{"key":"ref_24","first-page":"611","article-title":"Problem of Bathymetric Big Data Interpolation for Inland Mobile Navigation System","volume":"Volume 756","author":"Wawrzyniak","year":"2017","journal-title":"Communications in Computer and Information Science, Proceedings of the 23rd International Conference on Information and Software Technologies (ICIST 2017), Druskininkai, Lithuania, 12\u201314 October 2017"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wlodarczyk-Sielicka, M., and Lubczonek, J. (2019). The Use of an Artificial Neural Network to Process Hydrographic Big Data during Surface Modeling. Computer, 8.","DOI":"10.3390\/computers8010026"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1080\/01490419.2015.1053639","article-title":"Robust Automatic Reduction of Multibeam Bathymetric Data Based on M-estimators","volume":"38","author":"Rezvani","year":"2015","journal-title":"Mar. Geod."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s11001-012-9164-2","article-title":"The filtering and compressing of outer beams to multibeam bathymetric data","volume":"34","author":"Yang","year":"2013","journal-title":"Mar. Geophys. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.measurement.2014.12.025","article-title":"Method of establishing an underwater digital elevation terrain based on kriging interpolation","volume":"63","author":"Zhang","year":"2015","journal-title":"Measurement"},{"key":"ref_29","first-page":"441","article-title":"Importance of Neighborhood Parameters During Clustering of Bathymetric Data Using Neural Network","volume":"Volume 639","year":"2016","journal-title":"Communications in Computer and Information Science, Proceedings of the 22nd International Conference on Information and Software Technologies (ICIST 2016), Druskininkai, Lithuania, 13\u201315 October 2016"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lubczonek, J., and Borawski, M. (2016, January 2\u20134). A New Approach to Geodata Storage and Processing Based on Neural Model of the Bathymetric Surface. Proceedings of the 2016 Baltic Geodetic Congress (BGC Geomatics), Gdansk, Poland.","DOI":"10.1109\/BGC.Geomatics.2016.10"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1515\/pomr-2017-0088","article-title":"Application of an Autonomous\/Unmanned Survey Vessel (ASV\/USV) in Bathymetric Measurements","volume":"24","author":"Specht","year":"2017","journal-title":"Pol. Marit. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"77","DOI":"10.2478\/pomr-2013-0009","article-title":"A novel method for archiving multibeam sonar data with emphasis on efficient record size reduction and storage","volume":"20","author":"Moszynski","year":"2013","journal-title":"Pol. Marit. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1515\/geocart-2016-0007","article-title":"Neural networks for the generation of sea bed models using airborne lidar bathymetry data","volume":"65","author":"Kogut","year":"2016","journal-title":"Geod. Cartogr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s10596-013-9347-1","article-title":"Hydrographic data modeling methods for determining precise seafloor topography","volume":"17","author":"Aykut","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_35","first-page":"379","article-title":"New Optimum Dataset method in LiDAR processing","volume":"13","year":"2016","journal-title":"Acta Geodyn. Geomater."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"B\u0142aszczak-B\u0105k, W., Koppanyi, Z., and Toth, C. (2018). Reduction Method for Mobile Laser Scanning Data. ISPRS Int. J. Geo Inf., 7.","DOI":"10.3390\/ijgi7070285"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"75009","DOI":"10.1088\/1361-6501\/aa7444","article-title":"The OptD-multi method in LiDAR processing","volume":"28","author":"Kowalik","year":"2017","journal-title":"Meas. Sci. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1515\/pomr-2016-0026","article-title":"Technology of Spatial Data Geometrical Simplification in Maritime Mobile Information System for Coastal Waters","volume":"23","author":"Kazimierski","year":"2016","journal-title":"Pol. Marit. Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Stateczny, A., Gronska-Sledz, D., and Motyl, W. (2019). Precise Bathymetry as a Step Towards Producing Bathymetric Electronic Navigational Charts for Comparative (Terrain Reference) Navigation. J. Navig.","DOI":"10.1017\/S0373463319000377"},{"key":"ref_40","first-page":"5","article-title":"Fusion of data from GPS receivers based on a multi-sensor Kalman filter","volume":"3","author":"Borkowski","year":"2008","journal-title":"Transp. Probl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1109\/JOE.2012.2190810","article-title":"Position Error Correction for an Autonomous Underwater Vehicle Inertial Navigation System (INS) Using a Particle Filter","volume":"37","author":"Donovan","year":"2012","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1515\/pomr-2017-0004","article-title":"MSIS Image Positioning in Port Areas with the Aid of Comparative Navigation Methods","volume":"24","author":"Wawrzyniak","year":"2017","journal-title":"Pol. Marit. Res."},{"key":"ref_43","unstructured":"(2019, May 09). Ping DSP, Products Description. Available online: http:\/\/www.pingdsp.com\/3DSS-DX-450."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Stateczny, A., Wlodarczyk-Sielicka, M., Gronska, D., and Motyl, W. (2018, January 21\u201323). Multibeam Echosounder and Lidar in Process of 360\u00b0 Numerical Map Production for Restricted Waters with Hydrodron. Proceedings of the 2018 Baltic Geodetic Congress (BGC Geomatics) Gdansk, Olsztyn, Poland.","DOI":"10.1109\/BGC-Geomatics.2018.00061"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"112","DOI":"10.3138\/FM57-6770-U75U-7727","article-title":"Algorithms for the reduction of the number of points required to represent a digitized line or its caricature","volume":"10","author":"Douglas","year":"1973","journal-title":"Cartographica"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1080\/13658810701703001","article-title":"A three-dimensional Douglas\u2013Peucker algorithm and its application to automated generalization of DEMs","volume":"23","author":"Fei","year":"2009","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zeng, X., and He, W. (November, January 30). GPGPU Based Parallel processing of Massive LiDAR Point Cloud. Proceedings of the MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques. International Society for Optics and Photonics, Yichang, China.","DOI":"10.1117\/12.833740"},{"key":"ref_48","unstructured":"Chen, Y. (2012). High Performance Computing for Massive LiDAR Data Processing with Optimized GPU Parallel Programming. [Master\u2019s Thesis, The University of Texas at Dallas]."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cao, J., Cui, H., Shi, H., and Jiao, L. (2016). Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0157551"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5334","DOI":"10.1109\/TGRS.2018.2814012","article-title":"Deriving Bathymetry from Optical Images with a Localized Neural Network Algorithm","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","first-page":"1154","article-title":"Hybrid neural model of the sea bottom surface","volume":"Volume 3070","author":"Lubczonek","year":"2004","journal-title":"Lecture Notes in Computer Science, Proceedings of the International Conference on Artificial Intelligence and Soft Computing (ICAISC), Zakopane, Poland, 7\u201311 June 2004"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2245\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:24:46Z","timestamp":1760189086000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2245"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,26]]},"references-count":51,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11192245"],"URL":"https:\/\/doi.org\/10.3390\/rs11192245","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,9,26]]}}}