Autonomous Robots

, Volume 40, Issue 7, pp 1187–1205 | Cite as

Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

  • Yew Teck Tan
  • Mandar Chitre
  • Franz S. Hover


We present a cooperative bathymetry-based localization approach for a team of low-cost autonomous underwater vehicles (AUVs), each equipped only with a single-beam altimeter, a depth sensor and an acoustic modem. The localization of the individual AUV is achieved via fully decentralized particle filtering, with the local filter’s measurement model driven by the AUV’s altimeter measurements and ranging information obtained through inter-vehicle communication. We perform empirical analysis on the factors that affect the filter performance. Simulation studies using randomly generated trajectories as well as trajectories executed by the AUVs during field experiments successfully demonstrate the feasibility of the technique. The proposed cooperative localization technique has the potential to prolong AUV mission time, and thus open the door for long-term autonomy underwater.


Cooperative localization Autonomous underwater vehicle Rao-Blackwellized particle filter Acoustic ranging 



This work was supported by Singapore-MIT Alliance for Research and Technology (SMART) graduate fellowship. The authors wish to thank the Hovergroup, WAVES lab and STARFISH team for obtaining the experimental data, and Dr. Bharath Kaylan for providing bathymetric maps.


  1. Anonsen, K., & Hallingstad, O. (2006). Terrain aided underwater navigation using point mass and particle filters. In Position, location, and navigation symposium, 2006 IEEE/ION (pp. 1027–1035). doi: 10.1109/PLANS.2006.1650705.
  2. Arrichiello, F., Antonelli, G., Aguiar, A., & Pascoal, A. (2011). Observability metric for the relative localization of auvs based on range and depth measurements: Theory and experiments. In 2011 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3166–3171). doi: 10.1109/IROS.2011.6094466.
  3. Bahr, A., Leonard, J. J., & Fallon, M. F. (2009a). Cooperative localization for autonomous underwater vehicles. The International Journal of Robotics Research, 28(6), 714–728. doi: 10.1177/0278364908100561.CrossRefGoogle Scholar
  4. Bahr, A., Walter, M., & Leonard, J. (2009b). Consistent cooperative localization. In IEEE international conference on robotics and automation (ICRA), Kobe, Japan. doi: 10.1109/ROBOT.2009.5152859.
  5. Barkby, S., Williams, S. B., Pizarro, O., & Jakuba, M. V. (2011). A featureless approach to efficient bathymetric slam using distributed particle mapping. Journal of Field Robotics, 28(1), 19–39.CrossRefzbMATHGoogle Scholar
  6. Carreno, S., Wilson, P., Ridao, P., & Petillot, Y. (2010). A survey on terrain based navigation for AUVs. In OCEANS 2010 (pp. 1–7). doi: 10.1109/OCEANS.2010.5664372.
  7. Cover, T. M., & Thomas, J. A. (2006). Wiley series in telecommunications and signal processing. Elements of information theory (2nd ed.). New York: Wiley.Google Scholar
  8. Curcio, J., Leonard, J., & Patrikalakis, A. (2005). Scout: a low cost autonomous surface platform for research in cooperative autonomy. In OCEANS, 2005. Proceedings of MTS/IEEE (Vol. 1, pp. 725–729). doi: 10.1109/OCEANS.2005.1639838.
  9. Donovan, G. (2012). Position error correction for an autonomous underwater vehicle inertial navigation system (INS) using a particle filter. IEEE Journal of Oceanic Engineering, 37(3), 431–445. doi: 10.1109/JOE.2012.2190810.MathSciNetCrossRefGoogle Scholar
  10. Doucet, A., Freitas, Nd., Murphy, K. P., & Russell, S. J. (2000). Rao-blackwellised particle filtering for dynamic bayesian networks. In Proceedings of the 16th conference on uncertainty in artificial intelligence, UAI’00 (pp. 176–183). San Francisco, CA: Morgan Kaufmann Publishers Inc.Google Scholar
  11. Fairfield, N., & Wettergreen, D. (2008). Active localization on the ocean floor with multibeam sonar. In OCEANS 2008 (pp. 1–10). doi: 10.1109/OCEANS.2008.5151853.
  12. Fairfield, N., Kantor, G. A., & Wettergreen, D. (2006). Towards particle filter SLAM with three dimensional evidence grids in a flooded subterranean environment. In Proceedings of ICRA 2006 (pp. 3575–3580).Google Scholar
  13. Fallon, M. F., Kaess, M., Johannsson, H., & Leonard J. J. (2011). Efficient AUV navigation fusing acoustic ranging and side-scan sonar. In IEEE international conference on robotics and automation (ICRA), Shanghai.Google Scholar
  14. Fearnhead, P. (1998). Sequential monte carlo methods in filter theory, University of Oxford. PhD thesis.Google Scholar
  15. Gadre, A., & Stilwell, D. (2005). A complete solution to underwater navigation in the presence of unknown currents based on range measurements from a single location. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005) (pp. 1420–1425). doi: 10.1109/IROS.2005.1545230.
  16. Grisetti, G., Stachniss, C., & Burgard, W. (2007). Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Transactions on Robotics, 23(1), 34–46. doi: 10.1109/TRO.2006.889486.CrossRefGoogle Scholar
  17. Jakuba, M. V., Roman, C. N., Singh, H., Murphy, C., Kunz, C., Willis, C., et al. (2008). Long-baseline acoustic navigation for under-ice autonomous underwater vehicle operations. Journal of Field Robotics, 25(11–12), 861–879. doi: 10.1002/rob.20250.CrossRefGoogle Scholar
  18. Jiang, B., & Ravindran, B. (2011). Completely distributed particle filters for target tracking in sensor networks. In Parallel Distributed Processing Symposium (IPDPS), 2011 IEEE International (pp. 334–344). doi: 10.1109/IPDPS.2011.40.
  19. Kalyan, B., & Chitre, M. (2013). A feasibility analysis on using bathymetry for navigation of autonomous underwater vehicles. In Proceedings of the 28th Annual ACM Symposium on Applied Computing, ACM, New York, NY, SAC’13 (pp. 229–231). doi: 10.1145/2480362.2480411.
  20. Karlsson, R., & Gustafsson, F. (2003). Particle filter for underwater terrain navigation. In IEEE Workshop on Statistical Signal Processing (pp. 526–529). doi: 10.1109/SSP.2003.1289507.
  21. Koay, T. B., Tan, Y. T., Eng, Y. H., Gao, R., Chitre, M., Chew, J. L., et al. (2011). STARFISH-a small team of autonomous robotic fish. Indian Journal of Geo-Marine Sciences, 20(2), 157–167.Google Scholar
  22. Lanz, O. (2007). An information theoretic rule for sample size adaptation in particle filtering. In International conference on image analysis and processing (pp. 317–322).Google Scholar
  23. Liu, J. S., & Chen, R. (1998). Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association, 93, 1032–1044.MathSciNetCrossRefzbMATHGoogle Scholar
  24. Maczka, D., Gadre, A., & Stilwell, D. (2007). Implementation of a cooperative navigation algorithm on a platoon of autonomous underwater vehicles. In OCEANS 2007 (pp. 1–6). doi: 10.1109/OCEANS.2007.4449404.
  25. Maurya, P., Teixeira, F. C., & Pascoal, A. (2012). Complementary terrain/single beacon-based AUV navigation. In Proceedings of the IFAC workshop on navigation, guidance and control of underwater vehicles (NGCUV’2012), Porto (pp. 10–12).Google Scholar
  26. Meduna, D., Rock, S., & McEwen, R. (2010). Closed-loop terrain relative navigation for AUVs with non-inertial grade navigation sensors. In Autonomous underwater vehicles (AUV), 2010 IEEE/OES (pp. 1–8). doi: 10.1109/AUV.2010.5779659.
  27. Nordlund, P. J. (2002). Sequential Monte Carlo filters and integrated navigation, Linkopings universitet. PhD thesis.Google Scholar
  28. Nordlund, P. J., & Gustafsson, F. (2001). Sequential Monte Carlo filtering techniques applied to integrated navigation systems. In Proceedings of the American Control Conference (Vol. 6, pp. 4375–4380). doi: 10.1109/ACC.2001.945666.
  29. Nygren, I., & Jansson, M. (2004). Terrain navigation for underwater vehicles using the correlator method. IEEE Journal of Oceanic Engineering, 29(3), 906–915. doi: 10.1109/JOE.2004.833222.CrossRefGoogle Scholar
  30. Roman, C., & Singh, H. (2005). Improved vehicle based multibeam bathymetry using sub-maps and SLAM. In IEEE/RSJ International conference on intelligent robots and systems, (IROS 2005) (pp. 3662–3669).Google Scholar
  31. Rosencrantz, M., Gordon, G., & Thrun, S. (2003). Decentralized sensor fusion with distributed particle filters. In Proceedings of the nineteenth conference on uncertainty in artificial intelligence, UAI’03 (pp. 493–500) San Francisco, CA: Morgan Kaufmann Publishers Inc.Google Scholar
  32. Rui, G., & Chitre, M. (2010). Cooperative positioning using range-only measurements between two AUVs. In OCEANS 2010 IEEE-Sydney (pp. 1–6). doi: 10.1109/OCEANSSYD.2010.5603615.
  33. Schon, T., Gustafsson, F., & Nordlund, P. J. (2005). Marginalized particle filters for mixed linear/nonlinear state-space models. IEEE Transactions on Signal Processing, 53(7), 2279–2289. doi: 10.1109/TSP.2005.849151.MathSciNetCrossRefGoogle Scholar
  34. Sheng, X., Hu, Y. H., & Ramanathan, P. (2005). Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network. In Fourth international symposium on information processing in sensor networks, 2005. IPSN 2005 (pp. 181–188). doi: 10.1109/IPSN.2005.1440923.
  35. Smith, R. N., Chao, Y., Li, P. P., Caron, D. A., Jones, B. H., & Sukhatme, G. S. (2010). Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model. The International Journal of Robotics Research, 29(12), 1475–1497. doi: 10.1177/0278364910377243.CrossRefGoogle Scholar
  36. Song, T. L. (1999). Observability of target tracking with range-only measurements. IEEE Journal of Oceanic Engineering, 24(3), 383–387. doi: 10.1109/48.775299.CrossRefGoogle Scholar
  37. Tan, YT., Chitre, M. (2012). Direct policy search with variable-length genetic algorithm for single beacon cooperative path planning. In International symposium on distributed autonomous robotic systems (DARS). Baltimore.Google Scholar
  38. Tan, Y. T., Gao, R., & Chitre, M. (2014). Cooperative path planning for range-only localization using a single moving beacon. IEEE Journal of Oceanic Engineering, 39(2), 371–385. doi: 10.1109/JOE.2013.2296361.CrossRefGoogle Scholar
  39. Teixeira, F. C. (2007). Terrain-aided navigation and geophysical navigation of autonomous underwater vehicles. PhD thesis, Dynamical systems and ocean robotics lab, Lisbon.Google Scholar
  40. Teixeira, F. C., Pascoal, A., & Maurya, P. (2012a). A novel particle filter formulation with application to terrain-aided navigation. In Proceedings of IFAC workshop on navigation, guidance and control of underwater vehicles (NGCUV’2012) (pp 10–12). Porto.Google Scholar
  41. Teixeira, F. C., Quintas, J., & Pascoal, A. (2012b). AUV terrain-aided doppler navigation using complementary filtering. In Proceedings of IFAC conference on manoeuvring and control of marine craft (MCMC’2012). Arenzano.Google Scholar
  42. Vickery, K. (1998). Acoustic positioning systems. a practical overview of current systems. In Proceedings of the 1998 workshop on autonomous underwater vehicles, 1998. AUV’98 (pp. 5–17). doi: 10.1109/AUV.1998.744434.
  43. Webster, S., Walls, J., Whitcomb, L., & Eustice, R. (2013). Decentralized extended information filter for single-beacon cooperative acoustic navigation: Theory and experiments. IEEE Transactions on Robotics, 29(4), 957–974. doi: 10.1109/TRO.2013.2252857.CrossRefGoogle Scholar
  44. Webster, S. E., Eustice, R. M., Singh, H., & Whitcomb, L. L. (2012). Advances in single-beacon one-way-travel-time acoustic navigation for underwater vehicles. The International Journal of Robotics Research, 31(8), 935–950. doi: 10.1177/0278364912446166.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Acoustic Research Laboratory, Tropical Marine Science InstituteNational University of SingaporeSingaporeSingapore
  2. 2.Department of Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore
  3. 3.Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeUSA

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