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Visibility improvement in relation to turbidity and distance, and application to docking

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Abstract

Recently, autonomous underwater vehicles (AUVs) are being used in many applications. There is a limitation for underwater vehicle’s operation that takes a longer duration than the power capacity of underwater vehicles. Since underwater battery recharging station is supposed to be installed in the deep-sea bottom, the deep-sea docking experiments cannot avoid turbidity and dark environment. In this study, we propose a newly designed active 3D marker to improve the visibility of the 3D marker. A docking experiment apparatus was built. Two kinds of the 3D marker were used in the experiment, which were the active (light) and passive (non-light) 3D markers. The experimental results show the active 3D marker can be more recognizable in turbidity and dark environment than the passive 3D marker.

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References

  1. Jasper A (2012) Oil/gas pipeline leak inspection and repair in underwater poor visibility conditions: challenges and perspectives. J Environ Prot 3(05):394

    Article  Google Scholar 

  2. Kume A, Maki T, Sakamaki T, Ura T (2013) A method for obtaining high-coverage 3D images of rough seafloor using AUV-real-time quality evaluation and path-planning-. JRM 25(2):364–374

    Article  Google Scholar 

  3. Ribas D, Palomeras N, Ridao P, Carreras M, Mallios A (2011) Girona 500 auv: from survey to intervention. IEEE/ASME Trans Mech 17(1):46–53

    Article  Google Scholar 

  4. Krupinski S, Allibert G, Hua MD, Hamel T (2012) Pipeline tracking for fully-actuated autonomous underwater vehicle using visual servo control. In: 2012 American control conference (ACC), pp 6196–6202

  5. Yu SC, Ura T, Fujii T, Kondo H (2001) Navigation of autonomous underwater vehicles based on artificial underwater landmarks. In: MTS/IEEE oceans 2001. An ocean Odyssey. Conference proceedings (IEEE Cat. No. 01CH37295), vol 1, pp 409–416

  6. Cowen S, Briest S, Dombrowski J (1997) Underwater docking of autonomous undersea vehicles using optical terminal guidance. In: Oceans’ 97. MTS/IEEE conference proceedings, vol 2, pp 1143–1147

  7. Eustice RM, Pizarro O, Singh H (2008) Visually augmented navigation for autonomous underwater vehicles. IEEE J Ocean Eng 33(2):103–122

    Article  Google Scholar 

  8. Jung J, Choi S, Choi HT, Myung H (2016) Localization of AUVs using depth information of underwater structures from a monocular camera. In: 2016 13th International conference on ubiquitous robots and ambient intelligence (URAI), pp 444–446

  9. Ghosh S, Ray R, Vadali SRK, Shome SN, Nandy S (2016) Reliable pose estimation of underwater dock using single camera: a scene invariant approach. Mach Vis Appl 27(2):221–236

    Article  Google Scholar 

  10. Park JY, Jun BH, Lee PM, Oh J (2009) Experiments on vision guided docking of an autonomous underwater vehicle using one camera. Ocean Eng 36(1):48–61

    Article  Google Scholar 

  11. Feezor MD, Sorrell FY, Blankinship PR, Bellingham JG (2001) Autonomous underwater vehicle homing/docking via electromagnetic guidance. IEEE J Ocean Eng 26(4):515–521

    Article  Google Scholar 

  12. Negahdaripour S, Xu X, Khamene A (1998) A vision system for real-time positioning, navigation, and video mosaicing of sea floor imagery in the application of ROVs/AUVs. In: Proceedings fourth IEEE workshop on applications of computer vision. WACV’98 (Cat. No. 98EX201), pp 248–249

  13. Palomeras N, Pen̈alver A, Massot-Campos M, Negre P, Fernandez J, Ridao P, Oliver-Codina G, et al (2016) I-AUV docking and panel intervention at sea. Sensors 16(10):1673

    Article  Google Scholar 

  14. Maire FD, Prasser D, Dunbabin M, Dawson M (2009) A vision based target detection system for docking of an autonomous underwater vehicle. In: Proceedings of the 2009 Australasion conference on robotics and automation, Australian robotics and automation association

  15. Sagara S, Ambar RB, Takemura F (2013) A stereo vision system for underwater vehicle-manipulator systems-proposal of a novel concept using pan-tilt-slide cameras-. JRM 25(5):785–794

    Article  Google Scholar 

  16. Myint M, Yonemori K, Lwin KN, Yanou A, Minami M (2018) Dual-eyes vision-based docking system for autonomous underwater vehicle: an approach and experiments. J Intell Robot Syst 92(1):159–186

    Article  Google Scholar 

  17. Myint M, Yonemori K, Yanou A, Ishiyama S, Minami M (2015) Robustness of visual-servo against air bubble disturbance of underwater vehicle system using three-dimensional marker and dual-eye cameras. In: OCEANS 2015-MTS/IEEE Washington, pp 1–8

  18. Myint M, Yonemori K, Yanou A, Lwin KN, Minami M, Ishiyama S (2016) Visual servoing for underwater vehicle using dual-eyes evolutionary real-time pose tracking. JRM 28(4):543–558

    Article  Google Scholar 

  19. Myint M, Yonemori K, Yanou A, Lwin KN, Minami M, Ishiyama S (2016) Visual-based deep sea docking simulation of underwater vehicle using dual-eyes cameras with lighting adaptation. In: OCEANS 2016-Shanghai, pp 1–8

  20. Maki T, Shiroku R, Sato Y, Matsuda T, Sakamaki T, Ura T (2013) Docking method for hovering type AUVs by acoustic and visual positioning. In: 2013 IEEE international underwater technology symposium (UT), pp 1–6

  21. Lwin KN, Myint M, Mukada N, Yamada D, Matsuno T, Saitou K et al (2019) Sea docking by dual-eye pose estimation with optimized genetic algorithm parameters. J Intell Robot Syst 96(2):245–266

    Article  Google Scholar 

  22. Lwin KN, Mukada N, Myint M, Yamada D, Minami M, Matsuno T, Godou W (2018) Docking at pool and sea by using active marker in turbid and day/night environment. Artif Life Robot 23(3):409–419

    Article  Google Scholar 

  23. Garcia R, Gracias N (2011) Detection of interest points in turbid underwater images. In: OCEANS 2011 IEEE-Spain, pp 1–9

  24. Codevilla F, Gaya JDO, Duarte N, Botelho S (2004) Achieving turbidity robustness on underwater images local feature detection. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  25. Kanda Y, Mukada N, Yamada D, Lwin K, Myint M, Yamashita K, ..., Minami M (2018) Development and evaluation of active/lighting marker in turbidity and illumination variation environments. In: 2018 OCEANS-MTS/IEEE kobe techno-oceans (OTO), pp 1–7

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Authors and Affiliations

  1. Okayama University, 1-1-1 Tsushimanaka, Kita Ward, Okayama, 700-8530, Japan

    Horng-Yi Hsu, Yuichiro Toda, Keigo Watanabe & Mamoru Minami

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  1. Horng-Yi Hsu
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  2. Yuichiro Toda
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  3. Keigo Watanabe
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  4. Mamoru Minami
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Correspondence to Horng-Yi Hsu.

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This work was presented in part at the 24th International Symposium on Artificial Life and Robotics (Beppu, Oita, January 23–25, 2019).

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Hsu, HY., Toda, Y., Watanabe, K. et al. Visibility improvement in relation to turbidity and distance, and application to docking. Artif Life Robotics 25, 453–465 (2020). https://doi.org/10.1007/s10015-020-00606-6

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  • DOI: https://doi.org/10.1007/s10015-020-00606-6

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