Top > JRM > A Method for Obtaining High-Coverage 3D Images of Rough Seaflo ...

single-rb.php

JRM Vol.25 No.2 pp. 364-374
doi: 10.20965/jrm.2013.p0364
(2013)

Paper:

Views over last 60 days: 879

A Method for Obtaining High-Coverage 3D Images of Rough Seafloor Using AUV – Real-Time Quality Evaluation and Path-Planning –

Ayaka Kume, Toshihiro Maki, Takashi Sakamaki, and Tamaki Ura

Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan

Received:
October 12, 2012
Accepted:
February 15, 2013
Published:
April 20, 2013
Creative Commons License
Keywords:
AUV, real-time path-planning, 3D image mapping, next-best-view problem
Abstract
Autonomous Underwater Vehicles (AUVs) are often used for seafloor exploration, and some AUVs are now being deployed to obtain detailed photomosaics of the seafloor. However, it is difficult for the results to be evaluated on-site, so the image maps obtained often have unscanned areas caused by occlusions, disturbances, etc. In order to improve the coverage of a map, operators have to plan a new path and then redeploy the AUV. This process is quite timeconsuming and troublesome. The authors propose a new method for an AUV to obtain a full-coverage 3D image of a rough, unknown seafloor in a single deployment. First, the AUV observes the seafloor by following a pre-determined path. Second, the AUV calculates the following on-site and based on the data obtained: 3D bathymetry map, unscanned areas on the map, and the next path that can be taken to image the unscanned areas effectively. Then, the AUV follows the new path to obtain better results. The performance of this proposed method is verified in both tank experiments and by simulation. In the experiments, the AUV “Tri-TON” succeeds in generating a route for a second observation, and the coverage increases from 73% to 82%. The performance of the method on the actual seafloor is verified using the results of the tank experiments and the bathymetry data on a chimney in Kagoshima Bay, Japan.
Cite this article as:
A. Kume, T. Maki, T. Sakamaki, and T. Ura, “A Method for Obtaining High-Coverage 3D Images of Rough Seafloor Using AUV – Real-Time Quality Evaluation and Path-Planning –,” J. Robot. Mechatron., Vol.25 No.2, pp. 364-374, 2013.
Data files:
References
  1. [1] B. G. Allen, R. P. Stokey, T. C. Austin et al., “Remus: a small, low cost AUV; system description, field trials and performance results,” Proc. of OCEANS’97MTS/IEEE Conf., Vol.2, pp. 994-1000, 1997.
  2. [2] R. Marthiniussen, K. Vestgard, R. A. Klepaker, and N. Storkersen, “Hugin-auv concept and operational experiences to date,” OCEANS’04 MTTS/IEEE TECHNO-OCEAN’04, Vol.2, pp. 846-850, 2004.
  3. [3] T. Hyakudome et al., “Autonomous Underwater Vehicle for surveying deep ocean,” IEEE Int. Conf. on Industrial Technology 2009, 2009.
  4. [4] T. Ura, “Development of Autonomous Underwater Vehicle “r2D4” and its Operation off Sado and at Kurosima Knoll,” J. of the Robotics Society of Japan, Vol.22, No.6, pp. 709-713, 2004.
  5. [5] M. Johnson-Roberson, O. Pizarro, S. B. Williams, and I. Mahon, “Generation and visualization of large-scale three-dimensional reconstructions from underwater robotic surveys,” J. Field Robot., Vol.27, No.1, pp. 21-51, 2010.
  6. [6] S. B. Williams, O. Pizarro, M. Jakuba, and N. Barrett, “AUV Benthic Habitat Mapping in South Eastern Tasmania,” FSR 2009, 2009.
  7. [7] T. Maki, A. Kume, and T. Ura, “Volumetric mapping of tubeworm colonies in Kagoshima Bay through autonomous robotic surveys,” Deep Sea Research Part I: Oceanographic Research Papers, Vol.58, pp. 757-767, 2011.
  8. [8] T. Nakatani et al., “Seafloor imaging of methane seepage site of Kuroshima knoll by the AUV Tuna-Sand,” Proc. of 22nd meeting of Japan Society for Marine Surveys and Technology, pp. 24-25, 2010.
  9. [9] C. I. Connolly, “The Determination of Next Best Views,” Proc. IEEE Int. Conf. on Robotics & Automation. pp. 432-435, 1985.
  10. [10] D. R. Roberts and A. D. Marshall, “Viewpoint Selection for Complete Surface Coverage of Three Dimensional Objects,” In Proc. of the Britsh Machine Vision Conf., pp. 740-750, 1998.
  11. [11] V. Sequeira, J. Goncalves, and M. I. Ribeiro, “Active View Selection for Efficient 3D Scene Reconstruction,” Int. Conf. on Pattern Recognition, IEEE Computer Society, Vol.1, p. 815, 1996.
  12. [12] M. Otsuki and Y. Sato, “Viewpoint Selection of Rangefinder for 3D Shape Measurement,” The Trans. of the Institute of Electronics, Information and Communication Engineers, Vol.J81-D-2, No.6, pp. 1269-1277, Jun. 1998.
  13. [13] P. S. Blaer and P. K. Allen, “View planning and automated data acquisition for three-dimensional modeling of complex sites,” J. Field Robot., Vol.26, pp. 865-891, 2009.
  14. [14] M.-Y. Chan, W.-H. Mak, and H. Qu, “An Efficient Quality-Based Camera Path Planning Method for Volume Exploration,” Proc. of the 4th Int. Symposium on Advances in Visual Computing, Part II, ISVC ’08, pp. 12-21, 2008.
  15. [15] S. Slušný, M. Zerola, and R. Neruda, “Real Time Robot Path Planning and Cleaning,” Lecture Notes in Computer Science, Vol.62, pp. 442-449, Springer, 2010.
  16. [16] G. A. Hollinger, U. Mitra, and G. S. Sukhatme, “Active Classification: Theory and Application to Underwater Inspection,” CoRR, 1106.5829, 2011.
  17. [17] H. Choset and P. Pignon, “Coverage Path Planning: The Boustrophedon Decomposition,” Int. Conf. on Field and Service Robotics, 1997.
  18. [18] S. Hert, S. Tiwari, and V. Lumelsky, “A Terrain-Covering Algorithm for an AUV,” Autonomous Robots, Vol.3, pp. 91-119, 1996.
  19. [19] G. A. Hollinger, B. Englot, F. Hover, U. Mitra, and G. S. Sukhatme, “Uncertainty-Driven View Planning for Underwater Surface Inspection,” IEEE Int. Conf. on Robotics and Automation, 2012.
  20. [20] X. Sun, P. L. Rosin, R. R. Martin, and F. C. Langbein, “Fast and Effective Feature-Preserving Mesh Denoising,” IEEE Trans. Visualization and Computer Graphics, Vol.13, No.5, pp. 925-938, 2007.
  21. [21] M. Berg, M. Kreveld, M. Overmars et al., “Computational Geometry Algorithms and Applications,” Kindaikagakusha, 2000.
  22. [22] M. Garland and P. S. Heckbert, “Surface Simplification Using Quadric Error Metrics,” Computer Graphics (Proc. SIGGRAPH 97), ACM, pp. 209-216, 1997.
  23. [23] B. Korte and J. Vygen, “Combinatirial Optimization Theory and Algorithms,” Springer Japan, 2009.
  24. [24] T. Maki, T.Matsuda, A. Kume et al., “Development of the AUV Tri-TON for Imaging Rough Seafloor,” The 23rd Ocean Engineering Symposium, August 2-3, 2012.
  25. [25] B. Griod, G. Greiner, and H. Niemann, “Principles of 3D Image Analysis and Synthesis,” Springer US, 2000.
  26. [26] Y. Shiga, “Worldwide distribution of submarine hydrothermal deposits and their classification,” Resource geology, Vol.46, No.3, pp. 167-186, 1996.
  27. [27] K. Iizasa, “Formation of the Sunrise deposit,Myojin knoll Izu-Ogasa arc, Pacific,” JAMSTEC J. of Deep Sea Research Part II: Geology, Geochemistry, Geophysics and Dive Survey, 2000.
  28. [28] T. Maki, A. Kume, and T. Ura, “Volumetric mapping of tubeworm colonies in Kagoshima Bay through autonomous robotic surveys,” Deep Sea Research Part I: Oceanographic Research Papers, Vol.58, Issue 7, 2011.
  29. [29] T. Nakatani, S. Li, T. Ura et al., “3D visual modeling of hydrothermal chimneys using a rotary laser scanning system,” Underwater Technology, 2011.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

Copyright© 2013 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 18, 2024