Autonomous Robots

, Volume 40, Issue 7, pp 1207–1227 | Cite as

Mid-water current aided localization for autonomous underwater vehicles

  • Lashika Medagoda
  • Stefan B. Williams
  • Oscar Pizarro
  • James C. Kinsey
  • Michael V. Jakuba


Survey-class autonomous underwater vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the mid-water column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.


AUV ADCP Underwater Localization Mid-water Navigation 



This work is supported in part by NCRIS IMOS, the Australian Research Council (ARC), the New South Wales Government and the Woods Hole Oceanographic Institution. Sirius AUV data was obtained on cruises supported by the University of Tasmania and the IMOS AUV Facility program. We thank the cruise PIs (N. Barrett and C. Johnson), the officers and crew of the R/V Challenger and the Sirius operations team (D. Mercer and G. Powell). Deep water data was obtained on cruises AT26-09 (PIs: G. Wheat, A. Fisher, and S. Hulme) and AT26-17 (PIs: J. Kinsey, T. Crone, and E. Mittelsteadt) through funding from National Science Foundation. We thank the officers and crew of the R/V Atlantis and the Sentry operations team (Z. Berkowitz, A. Duester, J. Fujii, J. Hansen, M. Loebecker, S. Suman) for their assistance.


  1. Atkinson, C. (2008). Analysis of shipboard ADCP data from RRS Discovery Cruise D324: RAPID Array Eastern Boundary. Technical report: National Oceanography Centre Southampton.Google Scholar
  2. Brokloff, N. (1994). Matrix algorithm for Doppler sonar navigation. Brest, France, 2, 378–83.Google Scholar
  3. Brokloff, N. (1997). Dead reckoning with an ADCP and current extrapolation. In OCEANS 1997. MTS/IEEE conference proceedings (vol 2, pp. 994–1000)Google Scholar
  4. Brumley, B. H., Cabrera, R. G., Deines, K. L., & Terray, E. A. (1991). Performance of a broad-band acoustic Doppler current profiler. IEEE Journal of Oceanic Engineering, 16(4), 402–407.CrossRefGoogle Scholar
  5. Camilli, R., Reddy, C., Yoerger, D., Van Mooy, B., Jakuba, M., Kinsey, J., et al. (2010). Tracking hydrocarbon plume transport and biodegradation at Deepwater Horizon. Science, 330(6001), 201–204.CrossRefGoogle Scholar
  6. Caress, D. W., Clague, D. A., Paduan, J. B., Martin, J. F., Dreyer, B. M., Chadwick, W. W, Jr., et al. (2012). Repeat bathymetric surveys at 1-metre resolution of lava flows erupted at Axial Seamount in April 2011. Nature Geoscience, 5(7), 483–488. doi: 10.1038/ngeo1496.CrossRefGoogle Scholar
  7. Crees, T., Kaminski, C., Ferguson, J., Laframboise, J., Forrest, A., Williams, J., MacNeil, E., Hopkin, D., & Pederson, R. (2010). UNCLOS under ice survey—A historic AUV deployment in the Canadian high Arctic. In: IEEE/MTS Oceans (pp. 1–8)Google Scholar
  8. Flenniken, I. V. W. (2005). Modeling inertial measurement units and analyzing the effect of their errors in navigation applications. Masters Thesis, Auburn University.Google Scholar
  9. Fossen, T. (1994). Guidance and control of ocean vehicles. New York: Wiley.Google Scholar
  10. Furlong, M. E., Paxton, D., Stevenson, P., Pebody, M., McPhail, S. D., & Perrett, J. (2012). Autosub long range: A long range deep diving AUV for ocean monitoring. In Autonomous underwater vehicles (AUV), 2012 IEEE/OES (pp. 1–7).Google Scholar
  11. German, C., Yoerger, D., Jakuba, M., Shank, T., Langmuir, C., & Nakamura, K. (2008). Hydrothermal exploration with the autonomous Benthic explorer. In Deep-sea research part I-oceanographic research papers (vol. 55(2), pp. 203–219). doi: 10.1016/j.dsr.2007.11.004.
  12. Gordon, R. (1996). Principles of operation a practical primer. San Diego: RD Instruments.Google Scholar
  13. Healey, A., Rock, S., Cody, S., Miles, D., & Brown, J. (1995). Toward an improved understanding of thruster dynamics for underwater vehicles. IEEE Journal of Oceanic Engineering, 20(4), 354–361.CrossRefGoogle Scholar
  14. Hegrenaes, O., & Berglund, E. (2009). Doppler water-track aided inertial navigation for autonomous underwater vehicle. In OCEANS 2009-EUROPE, 2009. OCEANS’09 (pp. 1–100). doi: 10.1109/OCEANSE.2009.5278307.
  15. Hegrenaes, O., & Hallingstad, O. (2011). Model-aided INS with sea current estimation for robust underwater navigation. IEEE Journal of Oceanic Engineering, 36(2), 316–337.CrossRefGoogle Scholar
  16. Hobson, B. W., Bellingham, J. G., Kieft, B., McEwen, R., Godin, M., & Zhang, Y. (2012). Tethys-class long range AUVs—extending the endurance of propeller-driven cruising AUVs from days to weeks. In 2012 IEEE/OES autonomous underwater vehicles (AUV) (pp. 1–8).Google Scholar
  17. Hunt, M. M., Marquet, W. M., Moller, D. A., Peal, K. R., Smith, W. K., & Spindell, R. C. (1974). An acoustic navigation system. Technical report WHOI-74-6, Woods Hole Oceanographic Institution, Woods Hole, MA.Google Scholar
  18. iXSea (Accessed 22–03-2012) PHINS brochure.Google Scholar
  19. Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J., & Dellaert, F. (2011). isam2: Incremental smoothing and mapping with fluid relinearization and incremental variable reordering. In IEEE international conference on robotics and automation, IEEE (pp. 3281–3288).Google Scholar
  20. Kelley, D. S., Karson, J. A., Früh-Green, G. L., Yoerger, D. R., Shank, T. M., Butterfield, D. A., et al. (2005). A serpentinite-hosted ecosystem: The Lost City hydrothermal field. Science, 307(5714), 1428–1434.CrossRefGoogle Scholar
  21. Kinsey, J. C., & Whitcomb, L. L. (2004). Preliminary field experience with the DVLNAV integrated navigation system for oceanographic submersibles. Control Engineering Practice, 12(12), 1541–1548. invited Paper.CrossRefGoogle Scholar
  22. Kinsey, J. C., Eustice, R. M., & Whitcomb, L. L. (2006). A survey of underwater vehicle navigation: Recent advances and new challenges. In IFAC conference of manoeuvering and control of marine craft Google Scholar
  23. Kinsey, J. C., Yoerger, D. R., Jakuba, M. V., Camilli, R., Fisher, C. R., & German, C. R. (2011). Assessing the deepwater Horizon oil spill with the Sentry autonomous underwater vehicle. In IEEE/RSJ international conference on intelligent robots and systems (IROS), 2011, IEEE (pp. 261–267).Google Scholar
  24. Kinsey, J. C., Yang, Q., & Howland, J. C. (2014). Nonlinear dynamic model-based state estimators for underwater navigation of remotely operated vehicles. IEEE Transactions on Control Systems Technology, 99, 1–1.Google Scholar
  25. Lupton, T. (2010). Inertial slam with delayed initialisation. PhD Thesis, University of Sydney.Google Scholar
  26. Lupton, T., & Sukkarieh, S. (2009). Efficient integration of inertial observations into visual SLAM without initialization. In IEEE/RSJ international conference on intelligent robots and systems (pp. 1547–1552). doi: 10.1109/IROS.2009.5354267.
  27. Mahon, I., Williams, S., Pizarro, O., & Johnson-Roberson, M. (2008). Efficient view-based SLAM using visual loop closures. IEEE Transactions on Robotics, 24(5), 1002–1014. doi: 10.1109/TRO.2008.2004888.CrossRefGoogle Scholar
  28. Martin, S., & Whitcomb, L. (2008). Preliminary results in experimental identification of 3-dof coupled dynamical plant for underwater vehicles. In OCEANS 2008, IEEE (pp. 1–9).Google Scholar
  29. McPhail, S. D., & Pebody, M. (2009). Range-only positioning of a deep-diving autonomous underwater vehicle from a surface ship. IEEE Journal of Oceanic Engineering, 34(4), 669–677.CrossRefGoogle Scholar
  30. Medagoda, L., Jakuba, M. V., Pizarro, P., & Williams, W. (2010). Water column current profile aided localisation for autonomous underwater vehicles. In OCEANS 2010. Sydney: IEEE.Google Scholar
  31. Medagoda, L., Williams, S. B., Pizarro , O., & Jakuba, M. V. (2011). Water column current profile aided localisation combined with view-based SLAM for autonomous underwater vehicles. In International conference on robotics and automation 2011, IEEE, Shanghai.Google Scholar
  32. Medagoda, L., Eilders, M., & Kinsey, J. (2015). Autonomous underwater vehicle navigation in a spatiotemporally varying water current field. In IEEE international conference on robotics and automation (pp. 565–572).Google Scholar
  33. Napolitano, F. (2004). PHINS: THE AUTONOMOUS NAVIGATION SOLUTION. Sea TechnologyGoogle Scholar
  34. Nicholls, K., Abrahamsen, E., Buck, J., Dodd, P., Goldblatt, C., Griffiths, G., et al. (2006). Measurements beneath an Antarctic ice shelf using an autonomous underwater vehicle. Geophysical Research Letters, 33(8), doi: 10.1029/2006GL025998.
  35. Paull, L., Saeedi, S., Seto, M., & Li, H. (2014). AUV navigation and localization: A review. IEEE Journal of Oceanic Engineering Google Scholar
  36. Peyronnet, J. P., Person, R., & Rybicki, F. (1998). POSIDONIA 6000—a new long range highly accurate ultra short base line positioning system. Nice, France, 3, 1721–1727. doi: 10.1109/OCEANS.1998.726382.Google Scholar
  37. Schofield, O., Ducklow, H. W., Martinson, D. G., Meredith, M. P., Moline, M. A., & Fraser, W. R. (2010). How do polar marine ecosystems respond to rapid climate change? Science, 328(5985), 1520–1523.CrossRefGoogle Scholar
  38. Shih, H., Payton, C., Sprenke, J., & Mero, T. (2000). Towing basin speed calibration of acoustic Doppler current profiling instruments. In Joint conference on water resources engineering and water resources planning and management, American Society of Civil Engineers.Google Scholar
  39. Singh, H., Armstrong, R., Gilbes, F., Eustice, R., Roman, C., Pizarro, O., et al. (2004a). Imaging coral I: Imaging coral habitats with the seabed AUV. Subsurface Sensing Technologies and Applications, 5(1), 25–42.CrossRefGoogle Scholar
  40. Singh, H., Can, A., Eustice, R., Lerner, S., McPhee, N., Pizarro, O., et al. (2004b). Seabed AUV offers new platform for high-resolution imaging. EOS Transactions of the AGU, 85(31), 289–294.CrossRefGoogle Scholar
  41. Soon, B., Scheding, S., Lee, H., Lee, H., & Durrant-Whyte, H. (2008). An approach to aid INS using time-differenced GPS carrier phase (TDCP) measurements. Gps Solutions, 12(4), 261–271.CrossRefGoogle Scholar
  42. Stanway, M. (2011). Dead reckoning through the water column with an acoustic Doppler current profiler: Field experiences. In OCEANS 2011, IEEE (pp. 1–8).Google Scholar
  43. Stanway, M. (2012). Contributions to automated realtime underwater navigation. PhD Thesis, Massachusetts Institute of Technology.Google Scholar
  44. Titterton, D., & Weston, J. (2004). Strapdown inertial navigation technology. London: Peter Peregrinus Ltd.CrossRefGoogle Scholar
  45. Tivey, M. A., Johnson, H. P., Bradley, A. M., & Yoerger, D. R. (1998). Thickness of a submarine lava flow determined from near-bottom magnetic field mapping by autonomous underwater vehicle. Geophysical Research Letters, 25(6), 805–808.CrossRefGoogle Scholar
  46. Todd, R. E., Rudnick, D. L., Mazloff, M. R., Davis, R. E., & Cornuelle, B. D. (2011). Poleward flows in the southern california current system: Glider observations and numerical simulation. Journal of Geophysical Research: Oceans (1978–2012) 116(C2).Google Scholar
  47. van Graas, F., & Soloviev, A. (2004). Precise velocity estimation using a stand-alone GPS receiver. Navigation (Washington, DC), 51(4), 283–292.Google Scholar
  48. Visbeck, M. (2002). Deep velocity profiling using lowered acoustic Doppler current profilers: Bottom track and inverse solutions. Journal of Atmospheric and Oceanic Technology, 19(5), 794–807.CrossRefGoogle Scholar
  49. Walter, M., Eustice, R., & Leonard, J. (2007). Exactly sparse extended information filters for feature-based SLAM. The International Journal of Robotics Research, 26(4), 335–359.CrossRefGoogle Scholar
  50. Whitcomb, L. L., Yoerger, D. R., Singh, H., & Howland, J. (1999). Combined Doppler/LBL based navigation of underwater vehicles. In Proceedings of the the 11th international symposium on unmanned untethered submersible technology, Durham, NH.Google Scholar
  51. Williams, S., Pizarro, O., Mahon, I., & Johnson-Roberson, M. (2009). Simultaneous localisation and mapping and dense stereoscopic seafloor reconstruction using an AUV. In Experimental robotics. Berlin: Springer, (pp. 407–416).Google Scholar
  52. Williams, S., Pizarro, O., Webster, J., Beaman, R., Mahon, I., Johnson-Roberson, M., et al. (2010). Autonomous underwater vehicle-assisted surveying of drowned reefs on the shelf edge of the Great Barrier Reef. Australia. Journal of Field Robotics, 27(5), 675–697.CrossRefGoogle Scholar
  53. Williams, S., Pizarro, O., Jakuba, M., Johnson, C., Barrett, N., Babcock, R., et al. (2012). Monitoring of benthic reference sites: Using an autonomous underwater vehicle. IEEE Robotics Automation Magazine, 19(1), 73–84. doi: 10.1109/MRA.2011.2181772.CrossRefGoogle Scholar
  54. Yoerger, D., Jakuba, M., Bradley, A., & Bingham, B. (2007). Techniques for deep sea near bottom survey using an autonomous underwater vehicle. International Journal of Robotics Research, 26(1), 41–54.CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Lashika Medagoda
    • 1
  • Stefan B. Williams
    • 2
  • Oscar Pizarro
    • 2
  • James C. Kinsey
    • 3
  • Michael V. Jakuba
    • 3
  1. 1.Robotics Innovation Center, German Research Center for Artificial IntelligenceDFKI BremenBremenGermany
  2. 2.Australian Centre for Field RoboticsUniversity of SydneySydneyAustralia
  3. 3.Deep Submergence Laboratory, Woods Hole Oceanographic InstitutionApplied Ocean Physics & EngineeringWoods HoleUSA

Personalised recommendations