Skip to main content
SpringerLink
Log in

Stereo-vision-based AUV navigation system for resetting the inertial navigation system error

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

The Autonomous Underwater Vehicle (AUV) is used for underwater exploration of the sea floor. The AUV uses an Inertial Navigation System (INS) to recognize its position in the water, through the position estimation error of the INS increases with time. As the INS accumulated error increases, the success rate of the task decreases. Global Positioning System (GPS) is used for all kinds of vehicles moving on the ground or in the air; however, it cannot be widely utilized in water because radio signals cannot penetrate into the deep water. Therefore, how to eliminate the INS error is an important topic for the AUV. In this study, we propose a stereo-vision-based navigation system applied to the AUV to reset the integrated INS error. The experiments of the AUV navigation and returning to the docking station were conducted in the test tank by means of the INS and the stereo-vision system. The experimental results show that our proposed method is capable of docking the AUV and resetting the integrated INS error.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Balasuriya BAAP, et al (1997) Vision based autonomous underwater vehicle navigation: underwater cable tracking. Oceans’ 97 MTS/IEEE Conference Proceedings: 1418-1424

  2. McEwen Robert S et al (2008) Docking control system for a 54-cm-diameter (21-in) AUV. IEEE J Oceanic Eng 33(4):550–562

    Article  Google Scholar 

  3. Narcis Palomeras et al (2014) I-AUV docking and intervention in a subsea panel. IEEE/RSJ Int Conf Intell Robots Syst 2014:2279–2285

    Google Scholar 

  4. Morgado Marco, et al (2006) USBL/INS tightly-coupled integration technique for underwater vehicles. 2006 9th International Conference on Information Fusion: 1-8

  5. Hegrenaes \(\phi\)yvind, and Oddvar Hallingstad, (2011) Model-aided INS with sea current estimation for robust underwater navigation. IEEE J Oceanic Eng 36(2):316–337

  6. Enric Galceran et al (2012) A real-time underwater object detection algorithm for multi-beam forward looking sonar. IFAC Proc Vol 45(5):306–311

    Article  Google Scholar 

  7. Cowen Steve, Susan Briest, James Dombrowski (1997) Underwater docking of autonomous undersea vehicles using optical terminal guidance. Oceans’ 97 MTS/IEEE Conference Proceedings: 1143-1147

  8. Ken Teo, Goh Benjamin, Chai Oh Kwee (2014) Fuzzy docking guidance using augmented navigation system on an AUV. IEEE J OceanicEeng 40(2):349–361

    Google Scholar 

  9. Park Jin-Yeong, et al (2007) Experiment on underwater docking of an autonomous underwater vehicle ’ISiMI’ using optical terminal guidance. OCEANS 2007-Europe: 1-6

  10. Matthew Dunbabin, Lang Brenton, Wood Brett (2008) Vision-based docking using an autonomous surface vehicle. IEEE Int Conf Robotics Auto 2008:26–32

    Google Scholar 

  11. Myint Myo, et al (2015) Robustness of visual-servo against air bubble disturbance of underwater vehicle system using three-dimensional marker and dual-eye cameras. OCEANS 2015-MTS/IEEE Washington: 1-8

  12. Nwe Lwin Khin et al (2019) Sea docking by dual-eye pose estimation with optimized genetic algorithm parameters. J Intell Robotic Syst 96(2):245–266

    Article  Google Scholar 

  13. Myint Myo, et al (2016) Visual-based deep sea docking simulation of underwater vehicle using dual-eyes cameras with lighting adaptation. OCEANS 2016-Shanghai: 1-8

  14. Lwin Khin Nwe et al (2018) Visual docking against bubble noise with 3-D perception using dual-eye cameras. IEEE J Oceanic Eng 45(1):247–270

    Article  Google Scholar 

  15. Horng-Yi Hsu et al (2019) Improving pose estimation accuracy and expanding of visible space of lighting 3D marker in turbid water. IEEE Underwater Technol 2019:1–8

    Google Scholar 

  16. Hsu Horng-Yi et al (2020) Visibility improvement in relation to turbidity and distance, and application to docking. Artif Life Robot 25(3):453–465

    Article  Google Scholar 

  17. Myo Myint et al (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 

  18. Okamoto Akihiro, et al (2016) Development of hovering-type AUV gHOBALIN h for exploring seafloor hydrothermal deposits. OCEANS 2016 MTS/IEEE Monterey: 1-4

  19. Akihiro Okamoto et al (2019) Visual and Autonomous Survey of Hydrothermal Vents Using a Hovering Type AUV: Launching Hobalin Into the Western Offshore of Kumejima Island. Geochem Geophys Geosyst 20(12):6234–6243

    Article  Google Scholar 

  20. Nwe Lwin Khin et al (2018) Docking at pool and sea by using active marker in turbid and day/night environment. Artificial Life Robot 23(3):409–419

    Article  Google Scholar 

  21. Minami Mamoru, Julien Agbanhan, Toshiyuki Asakura (2003) Evolutionary scene recognition and simultaneous position/orientation detection. Soft computing in measurement and information acquisition: 178-207

  22. Wei Song, Minami Mamoru, Aoyagi Seiji (2008) On-line stable evolutionary recognition based on unit quaternion representation by motion-feedforward compensation. Int J Intell Comput Med Scie Image Process 2(2):127–139

    Google Scholar 

  23. Nakamura Sho, et al (2018) Development of Dual-eyes Docking System for AUV with Lighting 3D Marker. OCEANS 2018 MTS/IEEE Charleston: 1-9

Download references

Acknowledgements

The AUV used in this work is supported by the Cross-ministerial Strategic Innovation Promotion Program (SIP), “Next-generation technology for ocean resources exploration” (Lead agency: Japan Agency for Marine-Earth Science and Technology, JAMSTEC).

Author information

Author notes

  1. Horng Yi Hsu

    Present address: Okayama University, 1-1-1 Tsushimanaka, Kita Ward, 700-8530, Okayama, Japan

Authors and Affiliations

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

    Yuichiro Toda, Kohei Yamashita, Keigo Watanabe & Mamoru Minami

  2. National Maritime Research Institute, 6-38-1 Shinkawa, Mitaka, 181-0004, Tokyo, Japan

    Masahiko Sasano, Akihiro Okamoto & Shogo Inaba

Authors
  1. Horng Yi Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuichiro Toda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kohei Yamashita
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Keigo Watanabe
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Masahiko Sasano
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Akihiro Okamoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shogo Inaba
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mamoru Minami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Horng Yi Hsu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was presented in part at the 26th International Symposium on Artificial Life and Robotics (Online, January 21–23, 2021).

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hsu, H.Y., Toda, Y., Yamashita, K. et al. Stereo-vision-based AUV navigation system for resetting the inertial navigation system error. Artif Life Robotics 27, 165–178 (2022). https://doi.org/10.1007/s10015-021-00720-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-021-00720-z

Keywords

Search

Navigation