Skip to main content

Vision for the Marine Environment

  • Living reference work entry
  • First Online:
Encyclopedia of Robotics
  • 427 Accesses

Synonyms

Underwater computer vision; Underwater optical sensing and processing

Definition

Vision for the Marine Environment refers to underwater imaging hardware and algorithms that enable the perception of the subsea environment for marine science applications, inspection and intervention.

Overview

For a long time, optical cameras have been used in ROVs to provide the user with visual feedback of the operational scene. Conversely, AUVs have been traditionally equipped with sonar imaging systems, for two main reasons. First, the range of acoustic imaging is significantly higher, and second, as a consequence, they can work at a safer altitude, while the AUV follows the bottom profile. Nevertheless, during the last decades, vision systems have become smaller and more power-efficient, and the robot hardware has become more powerful and capable of storing the images onboard. Nowadays, commercial AUVs may be equipped with vision systems able to provide high-resolution seafloor imagery in...

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

Access this chapter

Institutional subscriptions

References

  • Bailey T, Durrant-Whyte H (2006) Simultaneous localization and mapping (SLAM): part II. IEEE Robot Autom Mag 13(3):108–117

    Article  Google Scholar 

  • Bingham B, Foley B, Singh H, Camilli R, Delaporta K, Eustice R, Mallios A, Mindell D, Roman CN, Sakellariou D (2010) Robotic tools for deep water archaeology: surveying an ancient shipwreck with an autonomous underwater vehicle. J Field Robot 27(6):702–717

    Article  Google Scholar 

  • Bodenmann A, Thornton B, Ura T (2017) Generation of high-resolution three-dimensional reconstructions of the seafloor in color using a single camera and structured light. J Field Robot 34(5):833–851

    Article  Google Scholar 

  • Bosch J, Gracias N, Ridao P, Ribas D (2015) Omnidirectional underwater camera design and calibration. Sensors 15(3):6033–6065

    Article  Google Scholar 

  • Bosch J, Gracias N, Ridao P, Istenič K, Ribas D (2016) Close-range tracking of underwater vehicles using light beacons. Sensors 16(4):429

    Article  Google Scholar 

  • Bryson M, Johnson-Roberson M, Pizarro O, Williams S (2012) Repeatable robotic surveying of marine benthic habitats for monitoring long-term change. In: Workshop on robotics for environmental monitoring at robotics: science and systems (RSS)

    Google Scholar 

  • Camilli R, Nomikou P, Escartin J, Ridao P, Mallios A, Kilias S, Argyraki A (2015) The Kallisti Limnes, carbon dioxide-accumulating subsea pools. Sci Rep, pp 1-9

    Google Scholar 

  • Campos R, Gracias N, Ridao P (2016) Underwater multi-vehicle trajectory alignment and mapping using acoustic and optical constraints. Sensors 16(3):387

    Article  Google Scholar 

  • Chantler MJ, Clark J, Umasuthan M (1997) Calibration and operation of an underwater laser triangulation sensor: the varying baseline problem. Opt Eng 36(9):2604

    Article  Google Scholar 

  • Chaves SM, Galceran E, Ozog P, Walls JM, Eustice RM (2017) Pose-graph SLAM for underwater navigation. Springer, Cham, pp 143–160

    Chapter  Google Scholar 

  • Corke P, Detweiler C, Dunbabin M, Hamilton M, Rus D, Vasilescu I (2007) Experiments with underwater robot localization and tracking. In: Proceedings 2007 IEEE international conference on robotics and automation, pp 4556–4561

    Google Scholar 

  • Dansereau DG, Mahon I, Pizarro O, Williams SB (2011) Plenoptic flow: closed-form visual odometry for light field cameras. In: 2011 IEEE/RSJ international conference on intelligent robots and systems, pp 4455–4462

    Google Scholar 

  • Durrant-Whyte H, Bailey T (2006) Simultaneous localization and mapping: part I. IEEE Robot Autom Mag 13(2):99–110

    Article  Google Scholar 

  • Escartin J, Barreyre T, Cannat M, Garcia R, Gracias N, Deschamps A, Salocchi A, Sarradin P-M, Ballu V (2015) Hydrothermal activity along the slow-spreading lucky strike ridge segment (Mid-Atlantic Ridge): distribution, heatflux, and geological controls. Earth Planet Sci Lett 431:173–185

    Article  Google Scholar 

  • Eustice R, Singh H, Leonard J, Walter M (2006a) Visually mapping the RMS Titanic: conservative covariance estimates for SLAM information filters. Int J Robot Res 25(12):1223–1242

    Google Scholar 

  • Eustice RM, Singh H, Leonard JJ (2006b) Exactly sparse delayed-state filters for view-based SLAM. IEEE Trans Robot 22(6):1100–1114

    Article  Google Scholar 

  • Ferreira F, Veruggio G, Caccia M, Bruzzone G (2012) A comparison between different feature-based methods for ROV vision-based speed estimation. In: Proceedings of the 3rd IFAC workshop on navigation, guidance and control of underwater vehicles, pp 325–330

    Article  Google Scholar 

  • Ferrer J, Elibol A, Delaunoy O, Gracias N, Garcia R (2007) Large-area photo-mosaics using global alignment and navigation data. In: Proceedings of the IEEE OCEANS conference, Vancouver

    Google Scholar 

  • Fraundorfer F, Scaramuzza D (2012) Visual odometry : part II: matching, robustness, optimization, and applications. IEEE Robot Autom Mag 19(2):78–90

    Article  Google Scholar 

  • Garcia R, Puig J, Ridao P, Cufi X (2002) Augmented State Kalman filtering for AUV navigation. In: Proceedings of 2002 IEEE international conference on robotics and automation, vol 4, pp 4010–4015

    Google Scholar 

  • Gracias N, Zwaan S, Bernardino A, Santos-Victor J (2003) Mosaic based navigation for autonomous underwater vehicles. J Oceanic Eng 28(4):609–624

    Article  Google Scholar 

  • Gracias N, Ridao P, Garcia R, Escartín J, L’Hour M, Cibecchini F, Campos R, Carreras M, Ribas D, Palomeras N, Magi L, Palomer A, Nicosevici T, Prados R, Hegedüs R, Neumann L, de Filippo F, Mallios A (2013) Mapping the Moon: using a lightweight AUV to survey the site of the 17th century ship ‘La Lune’. In: Proceedings of the IEEE OCEANS 2013 conference, Bergen

    Google Scholar 

  • Hildebrandt M, Kirchner F (2010) IMU-aided stereo visual odometry for ground-tracking AUV applications. In: Proceedings of the IEEE OCEANS 2010 conference – Sydney

    Google Scholar 

  • Horgan J, Toal D (2009) Computer vision applications in the navigation of unmanned underwater vehicles. In: Underwater vehicles (InTech)

    Google Scholar 

  • Inglis G, Smart C, Vaughn I, Roman C (2012) A pipeline for structured light bathymetric mapping. In: IEEE/RSJ international conference on intelligent robots and systems, pp 4425–4432

    Google Scholar 

  • Istenič K, Ila V, Polok L, Gracias N, García R (2017) Mission-time 3D reconstruction with quality estimation. In: Proceedings of the MTS/IEEE OCEANS 2017 conference, Aberdeen

    Google Scholar 

  • Johnson-Roberson M, Pizarro O, Willams S (2009) Towards large scale optical and acoustic sensor integration for visualization. In: Proceedings of the IEEE Oceans 2009-Europe conference, Bremen

    Google Scholar 

  • Kato H, Billinghurst M (1999) Marker tracking and HMD calibration for a video-based augmented reality conferencing system. In: Proceedings of the 2nd IEEE and ACM international workshop on augmented reality, IWAR’99. IEEE Computer Society, Washington, DC, pp 85–94

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  • Kocak D, Caimi F, Das P, Karson J (1999) A 3-D laser line scanner for outcrop scale studies of seafloor features. Proc OCEANS Conf 3:1105–1114

    Google Scholar 

  • Krupínski S, Allibert G, Hua MD, Hamel T (2017) An inertial-aided homography-based visual servo control approach for (almost) fully actuated autonomous underwater vehicles. IEEE Trans Robot 33(5):1041–1060

    Article  Google Scholar 

  • Lots JF, Lane DM, Trucco E, Chaumette F (2001) A 2D visual servoing for underwater vehicle station keeping. In: Proceedings 2001 ICRA IEEE international conference on robotics and automation, vol 3, pp 2767–2772

    Google Scholar 

  • Łuczyński T, Pfingsthorn M, Birk A (2017) The pinax-model for accurate and efficient refraction correction of underwater cameras in flat-pane housings. Ocean Eng 133(Supplement C):9–22

    Article  Google Scholar 

  • Marchand E, Chaumette F, Spindler F, Perrier M (2001) Controlling an uninstrumented ROV manipulator by visual servoing. In: MTS/IEEE Oceans 2001 conference, Honolulu, vol 2, pp 1047–1053

    Google Scholar 

  • Massot-Campos M, Oliver-Codina G (2014) Underwater laser-based structured light system for one-shot 3D reconstruction. In: IEEE sensors 2014 proceedings, pp 1138–1141

    Google Scholar 

  • Negahdaripour S, Zhang H, Firoozfam P, Oles J (2001) Utilizing panoramic views for visually guided tasks in underwater robotics applications. In Proceedings of the MTS/IEEE Oceans 2001 conference, Honolulu, vol 4, pp 2593–2600

    Google Scholar 

  • Ortiz A, Antich J, Oliver G (2011) A particle filter-based approach for tracking undersea narrow telecommunication cables. Mach Vis Appl 22(2):283–302

    Article  Google Scholar 

  • Palomer A, Ridao P, Ribas D, Forest J (2017) Underwater 3D laser scanners: the deformation of the plane. Springer International Publishing, Cham, pp 73–88

    Google Scholar 

  • Palomeras N, Nagappa S, Ribas D, Gracias N, Carreras M (2013) Vision-based localization and mapping system for AUV intervention. In: Proceedings of the IEEE oceans 2013 conference, Bergen

    Google Scholar 

  • Palomeras N, Peñalver A, Massot-Campos M, Negre PL, Fernández JJ, Ridao P, Sanz PJ, Oliver-Codina G (2016) I-AUV docking and panel intervention at sea. Sensors 16(10):1673

    Article  Google Scholar 

  • Pfingsthorn M, Birk A, Schwertfeger S, Buelow H, Pathak K (2010) Maximum likelihood mapping with spectral image registration. In: Proceedings of the IEEE international conference on robotics and automation ICRA2010, pp 4282–4287

    Google Scholar 

  • Prados R, Garcia R, Neumann L (2014) Image blending techniques and their application in underwate mosaicing. Springer, Cham

    Book  Google Scholar 

  • Prats M, Fernandez JJ, Sanz PJ (2012) Combining template tracking and laser peak detection for 3D reconstruction and grasping in underwater environments. IEEE Int Conf Intell Robot Syst 1:106–112

    Google Scholar 

  • Remondino F, Spera MG, Nocerino E, Menna F, Nex F, Gonizzi-Barsanti S (2013) Dense image matching: comparisons and analyses. In: Proceedings of IEEE conference on digital heritage, vol 1, pp 47–54

    Google Scholar 

  • Ridao P, Carreras M, Ribas D, Garcia R (2010) Visual inspection of hydroelectric dams using an autonomous underwater vehicle. J Field Robot 27(6):759–778

    Article  Google Scholar 

  • Roman C, Inglis G, Rutter J (2010) Application of structured light imaging for high resolution mapping of underwater archaeological sites. In: OCEANS 2010 IEEE – Sydney

    Google Scholar 

  • Singh H, Roman C, Pizarro O, Eustice R, Can A (2007) Towards high-resolution imaging from underwater vehicles. Int J Robot Res 26(1):55–74

    Article  Google Scholar 

  • Tarel J-P, Hautière N (2009) Fast visibility restoration from a single color or gray level image. In: Proceedings of IEEE international conference on computer vision (ICCV’09), Kyoto, pp 2201–2208

    Google Scholar 

  • Treibitz T, Schechner YY (2009) Active polarization descattering. IEEE Trans Pattern Anal Mach Intell 31(3):385–399

    Article  Google Scholar 

  • Vallicrosa G, Bosch J, Palomeras N, Ridao P, Carreras M, Gracias N (2016) Autonomous homing and docking for AUVs using range-only localization and light beacons. IFAC-PapersOnLine 49(23):54–60. 10th IFAC CAMS 2016

    Article  MathSciNet  Google Scholar 

  • Waechter M, Moehrle N, Goesele M (2014) Let there be color! large-scale texturing of 3D reconstructions. In: Proceedings of the 13th ECCV conference, Zurich, pp 836–850

    Google Scholar 

  • Weidemann A, Fournier G, Forand L, Mathieu P (2005) Optical image sensing through turbid water. In: Proceedings of SPIE, vol 5780, pp 59–70

    Google Scholar 

  • Williams S, Pizarro O, Jakuba M, Johnson C, Barrett N, Babcock R, Kendrick G, Steinberg P, Heyward A, Doherty P, Mahon I, Johnson-Roberson M, Steinberg D, Friedman A (2012) Monitoring of benthic reference sites: using an autonomous underwater vehicle. IEEE Robot Autom Mag 19(1):73–84

    Article  Google Scholar 

  • Wirth S, Carrasco P, Codina G (2013) Visual odometry for autonomous underwater vehicles. In: Proceedings of the MTS/IEEE Oceans 2013 conference, Bergen

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nuno Gracias .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer-Verlag GmbH Germany, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Gracias, N., Ridao, P., Garcia, R., Carreras, M. (2018). Vision for the Marine Environment. In: Ang, M., Khatib, O., Siciliano, B. (eds) Encyclopedia of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41610-1_17-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41610-1_17-1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41610-1

  • Online ISBN: 978-3-642-41610-1

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

Publish with us

Policies and ethics