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Artificial landmark-based underwater localization for AUVs using weighted template matching

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Abstract

This paper deals with vision-based localization techniques in structured underwater environments. For underwater robots, accurate localization is necessary to perform complex missions successfully, but few sensors are available for accurate localization in the underwater environment. Among the available sensors, cameras are very useful for performing short-range tasks despite harsh underwater conditions including low visibility, noise, and large areas of featureless scene. To mitigate these problems, we design artificial landmarks to be utilized with a camera for localization, and propose a novel vision-based object detection technique and apply it to the Monte Carlo localization (MCL) algorithm, a map-based localization technique. In the image processing step, a novel correlation coefficient using a weighted sum, multiple-template-based object selection, and color-based image segmentation methods are proposed to improve the conventional approach. In the localization step, to apply the landmark detection results to MCL, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform and the results are discussed.

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References

  1. Caccia M (2006) Laser-triangulation optical-correlation sensor for ROV slow motion estimation. IEEE J Ocean Eng 31(3):711–727

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. 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 

  4. Leabourne KN, Rock SM, Fleischer SD, Burton R (1997) Station keeping of an ROV using vision technology. In: Proceedings on MTS/IEEE OCEANS’97, vol 1, pp 634–640

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

    Article  Google Scholar 

  6. Nomoto M, Hattori M (1986) A deep ROV “DOLPHIN 3K”: design and performance analysis. IEEE J Ocean Eng 11(3):373–391

    Article  Google Scholar 

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

  8. Yoerger D, Newman J, Slotine JJ (1986) Supervisory control system for the Jason ROV. IEEE J Ocean Eng 11(3):392–400

    Article  Google Scholar 

  9. 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 

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

    Article  MATH  Google Scholar 

  11. 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

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

    Article  Google Scholar 

  13. Webster SE, Eustice RM, Singh H, Whitcomb LL (2012) Advances in single-beacon one-way-travel-time acoustic navigation for underwater vehicles. Int J Robot Res 31(8):935–950

    Article  Google Scholar 

  14. Yu SC, Ura T, Fujii T, Kondo H (2001) Navigation of autonomous underwater vehicles based on artificial underwater landmarks. In: Procedings on MTS/IEEE OCEANS 2001, pp 409–416

  15. 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 

  16. Dudek G, Jenkin M, Prahacs C, Hogue A, Sattar J, Giguere P, German A, Liu H, Saunderson S, Ripsman A et al (2005) A visually guided swimming robot. In: Procedings on IEEE/RSJ international conference on intelligent robotics and systems, pp 3604–3609

  17. Sattar J, Dudek G (2009) Robust servo-control for underwater robots using banks of visual filters. In: Procedings on IEEE international conference on robotics and automation, pp 3583–3588

  18. Negre A, Pradalier C, Dunbabin M (2008) Robust vision-based underwater homing using self-similar landmarks. J Field Robot 25(6–7):360–377

    Article  Google Scholar 

  19. 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 Australasian conference on robotics and automation. Australian Robotics and Automation Association, Sydney

  20. Lee D, Kim G, Kim D, Myung H, Choi HT (2012) Vision-based object detection and tracking for autonomous navigation of underwater robots. Ocean Eng 48:59–68

    Article  Google Scholar 

  21. Kim D, Lee D, Myung H, Choi HT (2012) Object detection and tracking for autonomous underwater robots using weighted template matching. In: Procedings on MTS/IEEE OCEANS 2012. IEEE, pp 1–5

  22. Balasuriya B, Takai M, Lam W, Ura T, Kuroda Y (1997) Vision based autonomous underwater vehicle navigation: underwater cable tracking. In: Procedings on MTS/IEEE OCEANS’97, vol 2, pp 1418–1424

  23. 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 

  24. Brunelli R (2009) Template matching techniques in computer vision: theory and practice. Wiley, New York

    Book  Google Scholar 

  25. Dellaert F, Fox D, Burgard W, Thrun S (1999) Monte Carlo localization for mobile robots. In: Procedings on IEEE international conference on robotics and automation, vol 2, pp 1322–1328

  26. Aulinas J, Carreras M, Llado X, Salvi J, Garcia R, Prados R, Petillot YR (2011) Feature extraction for underwater visual SLAM. In: Procedings on MTS/IEEE OCEANS 2011, pp 1–7

  27. Salvi J, Petillot Y, Thomas S, Aulinas J (2008) Visual slam for underwater vehicles using video velocity log and natural landmarks. In: Procedings on MTS/IEEE OCEANS 2008, pp 1–6

  28. Singh H, Can A, Eustice R, Lerner S, McPhee N, Pizarro O, Roman C (2004) Seabed AUV offers new platform for high-resolution imaging. Eos Trans Am Geophys Union 85(31):289–296

    Article  Google Scholar 

  29. Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal 22(11):1330–1334

    Article  Google Scholar 

  30. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal 24(5):603–619

    Article  Google Scholar 

  31. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Procedings on 6th international conference on computer vision, pp 839–846

  32. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  33. Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. In: Proceedings on European conference on computer vision, pp 404–417

  34. Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal 27(10):1615–1630

    Article  Google Scholar 

  35. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, vol 1, pp 886–893

  36. KoreaLPS. http://korealps.co.kr. Accessed 12 Apr 2014

  37. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This research was supported by Korea Institute of Ocean Science and Technology (KIOST) (Project title: “Development of technologies for an underwater robot based on artificial intelligence for highly sophisticated missions”) and by grant No. 10043928 from the Industrial Source Technology Development Programs of the MOTIE (Ministry Of Trade, Industry and Energy), Korea. The students are supported by Korea Ministry of Land, Transport and Maritime Affairs (MLTM) as U-City Master and Doctor Course Grant Program.

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Authors and Affiliations

  1. Urban Robotics Lab., Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, Republic of Korea

    Donghoon Kim, Donghwa Lee & Hyun Myung

  2. Ocean System Engineering Research Division, Korea Research Institute of Ships and Ocean Engineering (KRISO), 32 1312 Beon-gil, Yuseong-daero, Yuseong-gu, Daejeon, 305-343, Republic of Korea

    Hyun-Taek Choi

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  1. Donghoon Kim
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  2. Donghwa Lee
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  3. Hyun Myung
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  4. Hyun-Taek Choi
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Correspondence to Hyun Myung.

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Kim, D., Lee, D., Myung, H. et al. Artificial landmark-based underwater localization for AUVs using weighted template matching. Intel Serv Robotics 7, 175–184 (2014). https://doi.org/10.1007/s11370-014-0153-y

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  • DOI: https://doi.org/10.1007/s11370-014-0153-y

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