AUV-Based Multi-View Scanning Method for 3-D Reconstruction of Underwater Object Using Forward Scan Sonar

In this study, we propose an autonomous underwater vehicle (AUV)-based multi-directional scanning method of underwater objects using forward scan sonar (FSS). Recently, a method was developed that can generate a 3-D point cloud of an underwater object using FSS. However, the data comprised sparse and noisy characteristics that made it difficult for 3-D recognition. Another limitation was the absence of back and side surface information of an object. These limitations degraded the results of the 3-D reconstruction. We propose a multi-directional scanning strategy to improve the 3-D point cloud and reconstruction results where the AUV determines the direction of the next scan by analyzing the 3-D data of the object until the scanning is complete. This enables adaptive scanning based on the shape of the target object while reducing the amount of scanning time. Based on the scanning strategy, a polygonal approximation method for real-time 3-D reconstruction is developed to process scanned data groups of the 3-D point cloud. This process can efficiently handle multiple 3-D point cloud data for real-time operation and reduce its uncertainty. To verify the performance of our proposed method, simulations were performed with various objects and conditions. In addition, experiments were conducted in an indoor water tank, and the results were compared with the simulation results. Field experiments were conducted to verify the proposed method for more diverse environments and objects.

[1]  Mohammed Ghanbari,et al.  Scope of validity of PSNR in image/video quality assessment , 2008 .

[2]  Shahriar Negahdaripour,et al.  On Feature Matching and Image Registration for Two-dimensional Forward-scan Sonar Imaging , 2013, J. Field Robotics.

[3]  S. Negaharipour.  On 3-D scene interpretation from F-S sonar imagery , 2012, 2012 Oceans.

[4]  Hangil Joe,et al.  Crosstalk Noise Detection and Removal in Multi-beam Sonar Images Using Convolutional Neural Network , 2018, OCEANS 2018 MTS/IEEE Charleston.

[5]  Son-Cheol Yu,et al.  Development of imaging sonar based autonomous trajectory backtracking using AUVs , 2016, 2016 IEEE/OES Autonomous Underwater Vehicles (AUV).

[6]  E. Malis,et al.  3D Reconstruction of Natural Underwater Scenes Using the Stereovision System IRIS , 2007, OCEANS 2007 - Europe.

[7]  Y. Nose,et al.  AUV "TUNA-SAND" and its Exploration of hydrothermal vents at Kagoshima Bay , 2008, OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean.

[8]  R. Eustice,et al.  Large area 3D reconstructions from underwater surveys , 2004, Oceans '04 MTS/IEEE Techno-Ocean '04 (IEEE Cat. No.04CH37600).

[9]  Bjørn Jalving,et al.  DVL Velocity Aiding in the HUGIN 1000 Integrated Inertial Navigation System , 2004 .

[10]  Son-Cheol Yu,et al.  Profiling and Imaging Sonar Fusion Based 3D Normal Distribution Transform Mapping for AUV Application , 2018, 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO).

[11]  Maribel Yasmina Santos,et al.  Concave hull: A k-nearest neighbours approach for the computation of the region occupied by a set of points , 2007, GRAPP.

[12]  S. Negahdaripour,et al.  Forward-look 2-D sonar image formation and 3-D reconstruction , 2013, 2013 OCEANS - San Diego.

[13]  Son-Cheol Yu,et al.  Optimal Strategy for Seabed 3D Mapping of AUV Based on Imaging Sonar , 2018, 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO).

[14]  Shahriar Negahdaripour,et al.  Three-Dimensional Target Reconstruction From Multiple 2-D Forward-Scan Sonar Views by Space Carving , 2017, IEEE Journal of Oceanic Engineering.

[15]  Shahriar Negahdaripour,et al.  On 3-D Motion Estimation From Feature Tracks in 2-D FS Sonar Video , 2013, IEEE Transactions on Robotics.

[16]  Atsushi Yamashita,et al.  Development of acoustic camera-imaging simulator based on novel model , 2015, 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC).

[17]  Van-Dung Hoang,et al.  Automated architectural reconstruction using reference planes under convex optimization , 2016 .

[18]  V. T. Rajan,et al.  Voronoi diagrams of polygons: A framework for shape representation , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Son-Cheol Yu,et al.  Development of Simulator for Autonomous Underwater Vehicles utilizing Underwater Acoustic and Optical Sensing Emulators , 2018, 2018 18th International Conference on Control, Automation and Systems (ICCAS).

[20]  Reinhard Koch,et al.  3D reconstruction based on underwater video from ROV Kiel 6000 considering underwater imaging conditions , 2009, OCEANS 2009-EUROPE.

[21]  Son-Cheol Yu,et al.  AUV-Based Underwater 3-D Point Cloud Generation Using Acoustic Lens-Based Multibeam Sonar , 2018, IEEE Journal of Oceanic Engineering.

[22]  Son-Cheol Yu,et al.  Imaging sonar based real-time underwater object detection utilizing AdaBoost method , 2017, 2017 IEEE Underwater Technology (UT).

[23]  Jean-Daniel Boissonnat,et al.  Smooth surface reconstruction via natural neighbour interpolation of distance functions , 2002, Comput. Geom..

[24]  Andrea Trucco,et al.  From 3-D Sonar Images to Augmented Reality Models for Objects Buried on the Seafloor , 2008, IEEE Transactions on Instrumentation and Measurement.

[25]  Jonathan Richard Shewchuk,et al.  Delaunay refinement algorithms for triangular mesh generation , 2002, Comput. Geom..

[26]  Charles Perreault,et al.  Speckle Simulation Based on B-Mode Echographic Image Acquisition Model , 2007, Fourth Canadian Conference on Computer and Robot Vision (CRV '07).

[27]  M. R. Price,et al.  Autonomous underwater vehicles (AUVs) and investigations of the ice–ocean interface in Antarctic and Arctic waters , 2008 .

[28]  Son-Cheol Yu,et al.  Acoustic beam profile-based rapid underwater object detection for an imaging sonar , 2015 .

[29]  Daniel Cremers,et al.  LSD-SLAM: Large-Scale Direct Monocular SLAM , 2014, ECCV.

[30]  Son-Cheol Yu,et al.  Development of image sonar simulator for underwater object recognition , 2013, 2013 OCEANS - San Diego.

[31]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[32]  Son-Cheol Yu,et al.  Active Planning of AUVs for 3D Reconstruction of Underwater Object using Imaging Sonar , 2018, 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV).

[33]  Soon-Yong Park,et al.  Localization of an unmanned ground vehicle based on hybrid 3D registration of 360 degree range data and DSM , 2011 .

[34]  Son-Cheol Yu.  Development of real-time acoustic image recognition system using by autonomous marine vehicle , 2008 .

[35]  Jinwhan Kim,et al.  Three-Dimensional Reconstruction of a Marine Floating Structure With an Unmanned Surface Vessel , 2019, IEEE Journal of Oceanic Engineering.

[36]  Andrea Fusiello,et al.  Registration of Multiple Acoustic Range Views for Underwater Scene Reconstruction , 2002, Comput. Vis. Image Underst..

[37]  E. Belcher,et al.  Dual-Frequency Identification Sonar (DIDSON) , 2002, Proceedings of the 2002 Interntional Symposium on Underwater Technology (Cat. No.02EX556).

[38]  Wolfram Burgard,et al.  Real-time 3D visual SLAM with a hand-held camera , 2011 .

[39]  Son-Cheol Yu,et al.  Beam Slice-Based Recognition Method for Acoustic Landmark With Multi-Beam Forward Looking Sonar , 2017, IEEE Sensors Journal.

[40]  Tamaki Ura,et al.  Development of hovering type AUV “Cyclops” and its performance evaluation using image mosaicing , 2015 .

[41]  Andreas Birk,et al.  Spectral registration of noisy sonar data for underwater 3D mapping , 2011, Auton. Robots.

[42]  Murat D. Aykin,et al.  Modeling 2-D Lens-Based Forward-Scan Sonar Imagery for Targets With Diffuse Reflectance , 2016, IEEE Journal of Oceanic Engineering.

[43]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  Hassan Assalih.  3D reconstruction and motion estimation using forward looking sonar , 2013 .