Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (1): 180-186.doi: 10.13229/j.cnki.jdxbgxb20200783

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Realtime mosaic and visualization of 3D underwater acoustic seabed topography

Zhi-hua LI(),Ye-chao ZHANG,Guo-hua ZHAN   

  1. College of Information Science and Engineering，Hangzhou Normal University，Hangzhou 311121，China
  • Received:2020-10-15 Online:2022-01-01 Published:2022-01-14

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Abstract:

In this paper， a real-time mosaicing and visualization method for phased-array 3D acoustic topography is proposed. The translation and rotation matrix of sonar transducer carrier are obtained by differential GPS and attitude instrument as the initial iteration parameter， which eliminates the iteration time-consuming and mismatch problem. The obtained registration matrix converts the two adjacent acoustic images into the same coordinate system. Then the reference grid is used to rasterize the acoustic images to obtain cross point. In order to generate compatible data sets for surface reconstruction and reduce the computational complexity of mosaicking and reconstruction， the peak redundancy points in acoustic images are deleted. In order to accelerate mosaicing speed， the system adopts multi-thread parallel processing and GPU 3D image rendering acceleration architecture to balance the load between multiple CPU cores， and then uses GPU and VTK library to accelerate 3D image rendering. The results of indoor pool and lake experiments show that the method can effectively realize the real-time mosaicing and visualization of 3D acoustic topography.

Key words: artificial intelligence, image mosaic, marine surveying and mapping, underwater acoustic detection

CLC Number: 

  • TP391

Fig.1

128×128 digital beam of sonar receiver"

Fig.2

Realtime registration and mosaic for 3D under water acoustic seabed topography"

Fig.3

Rasterization for sonar image"

Fig.4

Peak redundancy removal"

Fig.5

Two points fusion in the same side and direction"

Fig.6

Virtual point generation in fusion process"

Fig.7

Index decision method"

Fig.8

3D acoustic system prototype and structure"

Fig.9

Comparison of registration residual"

Fig.10

Registration and mosaic results"

Fig.11

3D acoustic image mosaic sequence of Qiaodao Lake bottom"

Fig.12

Frame rate comparison of sonar image mosaic"

1 李振,彭华,姜景捷,等. 侧扫声呐在琼州海峡跨海通道地壳稳定性调查中的应用[J]. 地质力学学报, 2018, 24(2): 244-252.
Li Zhen, Peng Hua, Jiang Jing-jie, et al. Application of side scan sonar in the investigation of crustal stability of the cross-sea channel in the QiongZhou strait[J]. Journal of Geomechanics, 2018, 24(2): 244-252.
2 来向华,潘国富,苟诤慷,等. 侧扫声呐系统在海底管道检测中应用研究[J]. 海洋工程, 2011, 29(3): 117-121.
Lai Xiang-hua, Pan Guo-fu, Gou Zheng-kang, et al. Study on application of side scan sonar in submarine pipeline inspection[J]. The Ocean Engineering, 2011, 29(3): 117-121.
3 Negahdaripour S, Aykin M D, Sinnarajah S. Dynamic scene analysis and mosaicing of benthic habitats by FS sonar imaging——issues and complexities[C]∥MTS/IEEE Oceans,Hilton Waikoloa Village, USA, 2011: 1-7.
4 梅赛,高金耀,杨春国,等. 冲绳海槽南部多波束海底地形虚拟视景仿真初探[J]. 海洋测绘, 2010, 30(2): 19-23.
Mei Sai, Gao Jing-yao, Yang Chun-guo, et al. Initial exploration from virtual scene simulation of multibeam submarine topography in the south of the Okinawa Trough[J]. Hydrographic Surveying and Charging, 2010, 30(2): 19-23.
5 郭军,马桂云,马金凤,等. 一种针对侧扫声呐图像的数字镶嵌技术方法[J]. 测绘工程, 2017, 26(6): 34-39.
Guo Jun, Ma Gui-yun, Ma Jin-feng, et al. A digital mosaic method for side-scan images[J]. Engineering of Surveying and Mapping, 2017, 26(6): 34-39.
6 赵建虎,王爱学,王晓,等. 侧扫声呐条带图像分段拼接方法研究[J]. 武汉大学学报:信息科学版, 2013, 38(9): 1034-1038.
Zhao Jian-hu, Wang Ai-xue, Wang Xiao, et al. A segmented mosaic method for side scan sonar strip images using corresponding features[J]. Geomatics and Information Science of Wuhan University, 2013, 38(9): 1034-1038.
7 Hurtos N, Nagappa S, Palomeras N, et al. Real-time mosaicing with two-dimensional forward-looking sonar[C]//IEEE International Conference on Robotics and Automation, Hong Kong,China,2014: 601-606.
8 Hurtos N, Ribas D, Cufi X, et al. Fourier-based registration for robust forward-looking sonar mosaicing in low-visibility underwater environments[J]. Journal of Field Robotics, 2014, 32(1): 123-151.
9 Hurtos N, Cufi X, Petillot Y, et al. Fourier-based registrations for two-dimensional forward-looking sonar image mosaicing[C]∥IEEE/RSJ International Conference on Intelligent Robots and Systems, Algarve, Portugal, 2012: 5298–5305.
10 Zhang J, Sohel F, Bennamoun M, et al. NSCT-based fusion method for forward-looking sonar image mosaic[J]. IET Radar, Sonar and Navigation, 2017, 11(10): 1512-1522.
11 Zhang J, Sohel F, Bian H Y, et al. Forward-looking sonar image registration using polar transform[C]∥MTS/IEEE Oceans, Monterey, USA,2016: 1-6.
12 张健,卞红雨,张志刚. 基于FFT配准和多尺度融合的声呐图像拼接算法[J]. 南京大学学报:自然科学, 2017,53(4): 646-653.
Zhang Jian, Bian Hong-yu, Zhang Zhi-gang. Sonar image mosaic based on FFT registration and multi-scale fusion[J]. Journal of Nanjing University (Natural Sciences), 2017,53(4): 646-653.
13 Saucan A, Chonavel T. Model-based adaptive 3D sonar reconstruction in reverberating environment[J]. IEEE Transactions on Image Processing, 2015, 24(10):2928-2940.
14 Zhang L, Joucencel B, Fang Z, et al. 3D reconstruction of seabed surface through sonar data of AUVS[J]. Indian Journal of Geo-Marine Sciences, 2012, 41(6): 509-515.
15 Murino V, Trucco A. Three-dimensional image generation and processing in underwater acoustic vision[C]∥Proceedings of IEEE, Washington, USA, 2000: 1903-1948.
16 李海森,陈宝伟,么彬,等. 多子阵高分辨海底地形探测算法及其FPGA和DSP阵列实现[J]. 仪器仪表学报, 2010, 31(2): 281-285.
Li Hai-sen, Chen Bao-wei, Bin Me, et al. Implementation of high resolution sea bottom terrain detection method based on FPGA and DSP array[J]. Chinese Journal of Scientific Instrument, 2010, 31(2): 281-285.
17 Ikeuchi K, Wheeler M D. Iterative estimation of rotation and translation using quaternion[R]. Pittsburgh:Technical Report CMU-SC-95-215, 1995.
18 Cignoni P, Montani C, Scopigno R, et al. The Marching intersections algorithm for merging range images[R]. The Visual Computer, 2004, 20:149-164.
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