Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation - Publikacja - MOST Wiedzy

Wyszukiwarka

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Abstrakt

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.

Cytowania

  • 2 2

    CrossRef

  • 2 1

    Web of Science

  • 2 1

    Scopus

Cytuj jako

Pełna treść

pobierz publikację
pobrano 3 razy
Wersja publikacji
Accepted albo Published Version
Licencja
Creative Commons: CC-BY otwiera się w nowej karcie

Słowa kluczowe

Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Remote Sensing nr 11, strony 1 - 23,
ISSN: 2072-4292
Język:
angielski
Rok wydania:
2019
Opis bibliograficzny:
Stateczny A., Błaszczak-Bąk W., Sobieraj-Żłobińska A., Motyl W., Wiśniewska M.: Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation// Remote Sensing -Vol. 11,iss. 19 (2019), s.1-23
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/rs11192245
Bibliografia: test
  1. Chen, P.; Li, Y.; Su, Y.; Chen, X.; Jiang, Y. Review of AUV Underwater Terrain Matching Navigation. J. Navig. 2015, 68, 1155-1172. [CrossRef] otwiera się w nowej karcie
  2. Chen, P.-Y.; Li, Y.; Su, Y.-M.; Chen, X.-L.; Jiang, Y.-Q. Underwater terrain positioning method based on least squares estimation for AUV. China Ocean Eng. 2015, 29, 859-874. [CrossRef] otwiera się w nowej karcie
  3. Claus, B.; Bachmayer, R. Terrain-aided Navigation for an Underwater Glider. J. Field Robot. 2015, 32, 935-951. [CrossRef] otwiera się w nowej karcie
  4. Hagen, O.; Anonsen, K.; Saebo, T. Toward Autonomous Mapping with AUVs-Line-to-Line Terrain Navigation. In Proceedings of the Oceans 2015-MTS/IEEE Washington, Washington, DC, USA, 19-22 October 2015. otwiera się w nowej karcie
  5. Jung, J.; Li, J.; Choi, H.; Myung, H. Localization of AUVs using visual information of underwater structures and artificial landmarks. Intell. Serv. Robot. 2017, 10, 67-76. [CrossRef] otwiera się w nowej karcie
  6. Salavasidis, G.; Harris, C.; McPhail, S.; Phillips, A.B.; Rogers, E. Terrain Aided Navigation for Long Range AUV Operations at Arctic Latitudes. In Proceedings of the 2016 IEEE/OES Autonomous Underwater Vehicles (AUV), Tokyo, Japan, 6-9 November 2016; pp. 115-123. otwiera się w nowej karcie
  7. Li, Y.; Ma, T.; Wang, R.; Chen, P.; Zhang, Q. Terrain Correlation Correction Method for AUV Seabed Terrain Mapping. J. Navig. 2017, 70, 1062-1078. [CrossRef] otwiera się w nowej karcie
  8. Dong, M.; Chou, W.; Fang, B. Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data. Sci. Program. 2017, 2017, 7136702. [CrossRef] otwiera się w nowej karcie
  9. Song, Z.; Bian, H.; Zielinski, A. Application of acoustic image processing in underwater terrain aided navigation. Ocean Eng. 2016, 121, 279-290. [CrossRef] otwiera się w nowej karcie
  10. Ramesh, R.; Jyothi, V.B.N.; Vedachalam, N.; Ramadass, G.; Atmanand, M. Development and Performance Validation of a Navigation System for an Underwater Vehicle. J. Navig. 2016, 69, 1097-1113. [CrossRef] otwiera się w nowej karcie
  11. Li, Y.; Ma, T.; Chen, P.; Jiang, Y.; Wang, R.; Zhang, Q. Autonomous underwater vehicle optimal path planning method for seabed terrain matching navigation. Ocean Eng. 2017, 133, 107-115. [CrossRef] otwiera się w nowej karcie
  12. Li, Y.; Ma, T.; Wang, R.; Chen, P.; Shen, P.; Jiang, Y. Terrain Matching Positioning Method Based on Node Multi-information Fusion. J. Navig. 2017, 70, 82-100. [CrossRef] otwiera się w nowej karcie
  13. Stuntz, A.; Kelly, J.S.; Smith, R.N. Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation. Front. Robot. AI 2016, 3, 23. [CrossRef] otwiera się w nowej karcie
  14. Wang, L.; Yu, L.; Zhu, Y. Construction Method of the Topographical Features Model for Underwater Terrain Navigation. Pol. Marit. Res. 2015, 22, 121-125. [CrossRef] otwiera się w nowej karcie
  15. Remote Sens. 2019, 11, 2245 22 of 23 otwiera się w nowej karcie
  16. Wei, F.; Yuan, Z.; Zhe, R. UKF-Based Underwater Terrain Matching Algorithms Combination. In Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference, Xi'an, China, 10-11 January 2015; pp. 1027-1030. otwiera się w nowej karcie
  17. Zhou, L.; Cheng, X.; Zhu, Y. Terrain aided navigation for autonomous underwater vehicles with coarse maps. Meas. Sci. Technol. 2016, 27, 095002. [CrossRef] otwiera się w nowej karcie
  18. Zhou, L.; Cheng, X.; Zhu, Y.; Dai, C.; Fu, J. An Effective Terrain Aided Navigation for Low-Cost Autonomous Underwater Vehicles. Sensors 2017, 17, 680. [CrossRef] [PubMed] otwiera się w nowej karcie
  19. Zhou, L.; Cheng, X.; Zhu, Y.; Lu, Y. Terrain Aided Navigation for Long-Range AUVs Using a New Bathymetric Contour Matching Method. In Proceedings of the 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Busan, Korea, 7-11 July 2015. otwiera się w nowej karcie
  20. Calder, B.R.; Mayer, L.A. Automatic processing of high-rate, high-density multibeam echosounder data. Geochem. Geophys. Geosyst. 2003, 4, 1048. [CrossRef] otwiera się w nowej karcie
  21. Kulawiak, M.; Lubniewski, Z. Processing of LiDAR and multibeam sonar point cloud data for 3D surface and object shape reconstruction. In Proceedings of the 2016 Baltic Geodetic Congress (BGC Geomatics), Gdańsk, Poland, 2-4 June 2016. [CrossRef] otwiera się w nowej karcie
  22. Maleika, W. Moving Average Optimization in Digital Terrain Model Generation Based on Test Multibeam Echosounder Data. Geo Mar. Lett. 2015, 35, 61-68. [CrossRef] otwiera się w nowej karcie
  23. Maleika, W. The Influence of the Grid Resolution on the Accuracy of the Digital Terrain Model Used in Seabed Modelling. Mar. Geophys. Res. 2015, 36, 35-44. [CrossRef] otwiera się w nowej karcie
  24. Wlodarczyk-Sielicka, M. Interpolating Bathymetric Big Data for an Inland Mobile Navigation System. Inf. Technol. Control. 2018, 47, 338-348. [CrossRef] otwiera się w nowej karcie
  25. Wlodarczyk-Sielicka, M.; Wawrzyniak, N. Problem of Bathymetric Big Data Interpolation for Inland Mobile Navigation System. In Communications in Computer and Information Science, Proceedings of the 23rd International Conference on Information and Software Technologies (ICIST 2017), Druskininkai, Lithuania, 12-14 October 2017; otwiera się w nowej karcie
  26. Springer: Cham, Switzerland, 2017; Volume 756, pp. 611-621. [CrossRef] otwiera się w nowej karcie
  27. Wlodarczyk-Sielicka, M.; Lubczonek, J. The Use of an Artificial Neural Network to Process Hydrographic Big Data during Surface Modeling. Computer 2019, 8, 26. [CrossRef] otwiera się w nowej karcie
  28. Rezvani, M.-H.; Sabbagh, A.; Ardalan, A.A. Robust Automatic Reduction of Multibeam Bathymetric Data Based on M-estimators. Mar. Geod. 2015, 38, 327-344. [CrossRef] otwiera się w nowej karcie
  29. Yang, F.; Li, J.; Han, L.; Liu, Z. The filtering and compressing of outer beams to multibeam bathymetric data. Mar. Geophys. Res. 2013, 34, 17-24. [CrossRef] otwiera się w nowej karcie
  30. Zhang, T.; Xu, X.; Xu, S. Method of establishing an underwater digital elevation terrain based on kriging interpolation. Measurement 2015, 63, 287-298. [CrossRef] otwiera się w nowej karcie
  31. Wlodarczyk-Sielicka, M. Importance of Neighborhood Parameters During Clustering of Bathymetric Data Using Neural Network. In Communications in Computer and Information Science, Proceedings of the 22nd International Conference on Information and Software Technologies (ICIST 2016), Druskininkai, Lithuania, 13-15 October 2016; Springer: Cham, Switzerland, 2016; Volume 639, pp. 441-452. [CrossRef] otwiera się w nowej karcie
  32. Lubczonek, J.; Borawski, M. A New Approach to Geodata Storage and Processing Based on Neural Model of the Bathymetric Surface. In Proceedings of the 2016 Baltic Geodetic Congress (BGC Geomatics), Gdansk, Poland, 2-4 June 2016; pp. 1-7. [CrossRef] otwiera się w nowej karcie
  33. Specht, C.;Świtalski, E.; Specht, M. Application of an Autonomous/Unmanned Survey Vessel (ASV/USV) in Bathymetric Measurements. Pol. Marit. Res. 2017, 24, 36-44. [CrossRef] otwiera się w nowej karcie
  34. Moszynski, M.; Chybicki, A.; Kulawiak, M.; Lubniewski, Z. A novel method for archiving multibeam sonar data with emphasis on efficient record size reduction and storage. Pol. Marit. Res. 2013, 20, 77-86. [CrossRef] otwiera się w nowej karcie
  35. Kogut, T.; Niemeyer, J.; Bujakiewicz, A. Neural networks for the generation of sea bed models using airborne lidar bathymetry data. Geod. Cartogr. 2016, 65, 41-54. [CrossRef] otwiera się w nowej karcie
  36. Aykut, N.O.; Akpınar, B.; Aydın, Ö. Hydrographic data modeling methods for determining precise seafloor topography. Comput. Geosci. 2013, 17, 661-669. [CrossRef] otwiera się w nowej karcie
  37. Blaszczak-Bak, W. New Optimum Dataset method in LiDAR processing. Acta Geodyn. Geomater. 2016, 13, 379-386. [CrossRef] otwiera się w nowej karcie
  38. Błaszczak-Bąk, W.; Koppanyi, Z.; Toth, C. Reduction Method for Mobile Laser Scanning Data. ISPRS Int. J. Geo Inf. 2018, 7, 285. [CrossRef] otwiera się w nowej karcie
  39. Błaszczak-Bąk, W.; Sobieraj-Żłobińska, A.; Kowalik, M. The OptD-multi method in LiDAR processing. Meas. Sci. Technol. 2017, 28, 75009. [CrossRef] otwiera się w nowej karcie
  40. Kazimierski, W.; Wlodarczyk-Sielicka, M. Technology of Spatial Data Geometrical Simplification in Maritime Mobile Information System for Coastal Waters. Pol. Marit. Res. 2016, 23, 3-12. [CrossRef] otwiera się w nowej karcie
  41. Stateczny, A.; Gronska-Sledz, D.; Motyl, W. Precise Bathymetry as a Step Towards Producing Bathymetric Electronic Navigational Charts for Comparative (Terrain Reference) Navigation. J. Navig. 2019. [CrossRef] otwiera się w nowej karcie
  42. Borkowski, P.; Pietrzykowski, Z.; Magaj, J.; Mąka, M. Fusion of data from GPS receivers based on a multi-sensor Kalman filter. Transp. Probl. 2008, 3, 5-11.
  43. Donovan, G.T. Position Error Correction for an Autonomous Underwater Vehicle Inertial Navigation System (INS) Using a Particle Filter. IEEE J. Ocean. Eng. 2012, 37, 431-445. [CrossRef] otwiera się w nowej karcie
  44. Wawrzyniak, N.; Stateczny, A. MSIS Image Positioning in Port Areas with the Aid of Comparative Navigation Methods. Pol. Marit. Res. 2017, 24, 32-41. [CrossRef] otwiera się w nowej karcie
  45. Ping DSP, Products Description. Available online: http://www.pingdsp.com/3DSS-DX-450 (accessed on 9 May 2019).
  46. Stateczny, A.; Wlodarczyk-Sielicka, M.; Gronska, D.; Motyl, W. Multibeam Echosounder and Lidar in Process of 360 • Numerical Map Production for Restricted Waters with Hydrodron. In Proceedings of the 2018 Baltic Geodetic Congress (BGC Geomatics) Gdansk, Olsztyn, Poland, 21-23 June 2018. [CrossRef] otwiera się w nowej karcie
  47. Douglas, D.; Peucker, T. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica 1973, 10, 112-122. [CrossRef] otwiera się w nowej karcie
  48. Fei, L.; Jin, H. A three-dimensional Douglas-Peucker algorithm and its application to automated generalization of DEMs. Int. J. Geogr. Inf. Sci. 2009, 23, 703-718. [CrossRef] otwiera się w nowej karcie
  49. Zeng, X.; He, W. GPGPU Based Parallel processing of Massive LiDAR Point Cloud. In Proceedings of the MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques. International Society for Optics and Photonics, Yichang, China, 30 October-1 November 2009; Volume 7497. otwiera się w nowej karcie
  50. Chen, Y. High Performance Computing for Massive LiDAR Data Processing with Optimized GPU Parallel Programming. Master's Thesis, The University of Texas at Dallas, Richardson, TX, USA, 2012.
  51. Cao, J.; Cui, H.; Shi, H.; Jiao, L. Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce. PLoS ONE 2016, 11, e0157551. [CrossRef] [PubMed] otwiera się w nowej karcie
  52. Liu, S.; Wang, L.; Liu, H.; Su, H.; Li, X.; Zheng, W. Deriving Bathymetry from Optical Images with a Localized Neural Network Algorithm. IEEE Trans. Geosci. Remote Sens. 2018, 56, 5334-5342. [CrossRef] otwiera się w nowej karcie
  53. Lubczonek, J. Hybrid neural model of the sea bottom surface. In Lecture Notes in Computer Science, Proceedings of the International Conference on Artificial Intelligence and Soft Computing (ICAISC), Zakopane, Poland, 7-11 June 2004; Springer: Berlin/Heidelberg, Germany, 2004; Volume 3070, pp. 1154-1160. otwiera się w nowej karcie
  54. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). otwiera się w nowej karcie
Weryfikacja:
Politechnika Gdańska

wyświetlono 108 razy

Publikacje, które mogą cię zainteresować

Meta Tagi