Skip to main content

Advertisement

Log in

Localization of AUVs using visual information of underwater structures and artificial landmarks

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

Abstract

Autonomous underwater vehicles (AUVs) can perform flexible operations in complex underwater environments due to their autonomy. Localization is one of the key components of autonomous navigation. Since the inertial navigation system of an AUV suffers from drift, observing fixed objects in an inertial reference system can enhance the localization performance. In this paper, we propose a method of localizing AUVs by exploiting visual measurements of underwater structures and artificial landmarks. In a framework of particle filtering, a camera measurement model that emulates the camera’s observation of underwater structures is designed. The particle weight is then updated based on the extracted visual information of the underwater structures. Detected artificial landmarks are also used in the particle weight update. The proposed method is validated by experiments performed in a structured basin environment.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Caccia M (2007) Vision-based ROV horizontal motion control: near-seafloor experimental results. Control Eng Pract 15(6):703–714

    Article  Google Scholar 

  2. Ferreira F, Veruggio G, Caccia M, Bruzzone G (2012) Real-time optical SLAM-based mosaicking for unmanned underwater vehicles. Intell Serv Robot 5(1):55–71

    Article  Google Scholar 

  3. Leabourne KN, Rock SM, Fleischer SD, Burton R (1997) Station keeping of an ROV using vision technology. Proc MTS/IEEE OCEANS97 1:634–640

    Article  Google Scholar 

  4. Negahdaripour S, Firoozfam P (2006) An ROV stereo vision system for ship-hull inspection. IEEE J Ocean Eng 31(3):551–564

    Article  Google Scholar 

  5. Whitcomb L, Yoerger D, Singh H, Howland J (1999) Advances in underwater robot vehicles for deep ocean exploration: navigation, control, and survey operations. In: 9th international symposium on robotics research, pp 346–353

  6. Hollinger GA, Englot B, Hover FS, Mitra U, Sukhatme GS (2013) Active planning for underwater inspection and the benefit of adaptivity. Int J Robot Res 32(1):3–18

    Article  Google Scholar 

  7. Jun BH, Park JY, Lee FY, Lee PM, Lee CM, Kim K, Lim YK, Oh JH (2009) Development of the AUV ISiMI and a free running test in an ocean engineering basin. Ocean Eng 36(1):2–14

    Article  Google Scholar 

  8. Kim A, Eustice R (2009) Pose-graph visual SLAM with geometric model selection for autonomous underwater ship hull inspection. In: Procedings on IEEE/RSJ international conference on intelligent robotics and systems, pp 1559–1565

  9. Marani G, Choi S (2010) Underwater target localization. IEEE Robot Autom Mag 17(1):64–70

    Article  Google Scholar 

  10. Kim DH, Lee DH, Myung H, Choi HT (2014) Artificial landmark-based underwater localization for AUV using weighted template matching. Intell Serv Robot 7(3):175–184

    Article  Google Scholar 

  11. Paull L (2014) AUV navigation and localization: a review. IEEE J Ocean Eng 93(1):131–149

    Article  Google Scholar 

  12. Fallon MF, Kaess M, Johannsson H, Leonard JJ (2011) Efficient AUV navigation fusing acoustic ranging and side-scan sonar. In: Proceedings of the IEEE international conference on robotics and automation, pp 2398–2405

  13. Johannsson H, Kaess M, Englot B, Hover F, Leonard JJ (2010) Imaging sonar-aided navigation for autonomous underwater harbor surveillance. In: Proceedings of the IEEE/RSJ international conference on Intelligent and Robotic Systems, pp 4396–4403

  14. Bulow H, Birk A (2011) Spectral registration of noisy sonar data for underwater 3D mapping. Auton Robot 30:307–331

    Article  Google Scholar 

  15. Pfingsthorn M, Birk A, Bulow H (2012) Uncertainty estimation for a 6-DOF spectral registration method as basis for sonar-based under- water 3D SLAM. In: Proceedings of the international conference on robotics and automation, pp 3049–3054

  16. Pathak K, Birk A, Vaskevicius N (2010) Plane-based registration of sonar data for underwater 3D mapping. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, pp 4880–4885

  17. Balasuriya B, Takai M, Lam W, Ura T, Kuroda Y (1997) Vision based autonomous underwater vehicle navigation: underwater cable tracking. Proc MTS/IEEE OCEANS97 2:1418–1424

    Article  Google Scholar 

  18. Hover FS, Eustice RM, Kim A, Englot B, Johannsson H, Kaess M, Leonard JJ (2012) Advanced perception, navigation and planning for autonomous in-water ship hull inspection. Int J Robot Res 31(12):1445–1464

    Article  Google Scholar 

  19. Kim A, Eustice R (2013) Real-time visual SLAM for autonomous underwater hull inspection using visual saliency. IEEE T Robot 29(3):719–733

    Article  Google Scholar 

  20. Lowe DG (1999) Object recognition from local scale-invariant features. Proc IEEE Int Conf Comput Vis 2:1150–1157

    Google Scholar 

  21. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Und 110:346–359

    Article  Google Scholar 

  22. Maurice FF, Johannsson H, Leonard JJ (2012) Efficient scene simulation for robust Monte Carlo localization using an RGB-D camera. In: IEEE international conference on robotics and automation, pp 1663–1670

  23. Gerard P, Gagalowicz A (2000) Three dimensonal model-based tracking using texture learning and matching. Pattern Recogn Lett 21:1095–1103

    Article  MATH  Google Scholar 

  24. Noyer J, Lanvin P, Benjelloun M (2004) Model-based tracking of 3D objects based on a sequential Monte-Carlo method. Conf Signals Syst Comput 2:1744–1748

    Google Scholar 

  25. Zang C, Hashimoto K (2011) Camera localization by CAD model matching. In: 2011 IEEE/SICE international symposium on system integration, pp 30–35

  26. Hoermann S, Borges P (2014) Vehicle localization and classification using off-board vision and 3-D models. IEEE T Robot 30(2):432–447

    Article  Google Scholar 

  27. Kondo H, Maki T, Ura T, Nose Y, Sakamaki T, Inaishi M (2004) Relative navigation of an autonomous underwater vehicle using a light-section profiling system. In: Proceedings of the international conference on intelligent robots and systems, pp 1103–1108

  28. Thrun S, Burgard W, Fox D (2005) Probabilistic robotics. MIT press, Cambridge

    MATH  Google Scholar 

  29. Dellaert F, Fox D, Burgard W, Thrun S (1999) Monte Carlo localization for mobile robots. Proc IEEE Int Conf Robot Autom 2:1322–1328

    Article  MATH  Google Scholar 

  30. Li JH, Lee MJ, Kim JG, Kim JT, Suh J (2014) Development of P-SURO II hybrid AUV and its experimental study. In: Proceedings of the OCEANS, pp 1–6

  31. Fernandez-Madrigal J, Claraco J (2013) Simultaneous localization and mapping for mobile robots: introduction and methods. Information Science Reference, Hershey, PA

    Book  Google Scholar 

  32. http://www.vision.caltech.edu/bouguetj/calib_doc/. Accessed 4 Nov 2016

  33. Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE T Pattern Anal 6(13):583–598

    Article  Google Scholar 

  34. Ilea DE, Whelan PF (2006) Color image segmentation using a spatial k-means clustering algorithm. In: IMVIP 2006—10th international machine vision and image processing conference, pp 1–8

  35. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Ssstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE T Pattern Anal 34(11):2274–2282

    Article  Google Scholar 

  36. Lee YJ, Lee JH, Choi HT (2014) A framework of recognition and tracking for underwater objects based on sonar images: part 1. Design and recognition of artificial landmark considering characteristics of sonar images. J Inst Electron Inf Eng 51(2):422–429 In Korean

    Google Scholar 

  37. Olson E (2011) AprilTag: a robust and flexible visual fiducial system. In: Procedings on IEEE international conference on robotics and automation, pp 3400–3407

Download references

Acknowledgements

This research was supported in part by grant No. 10043928 from the Industrial Source Technology Development Programs of the MOTIE (Ministry Of Trade, Industry & Energy), Korea, and in part by the project, Development of basic SLAM technologies for autonomous underwater robot and software environment for MOSS-IvP sponsored by Korea Research Institute of Ships & Ocean Engineering (KRISO). The students are supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as U-City Master and Doctor Course Grant Program.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hyun Myung.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jung, J., Li, JH., Choi, HT. et al. Localization of AUVs using visual information of underwater structures and artificial landmarks. Intel Serv Robotics 10, 67–76 (2017). https://doi.org/10.1007/s11370-016-0210-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-016-0210-9

Keywords

Navigation