Skip to main content
SpringerLink
Log in

Map-based localization and loop-closure detection from a moving underwater platform using flow features

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

In recent years, flow sensing has gotten the attention of the robotics community as an exteroceptive sensing modality, in addition to the conventional underwater sensing modalities of vision and sonar. Earlier works on flow sensing for robotics focus on detection and characterization of objects’ wakes, with the focus slowly evolving towards more complicated tasks such as localization of a stationary underwater platform using flow. In this paper we take this one step ahead, and present map-based localization and loop-closure detection from a continuously moving platform. Map-based localization is performed using flow features inside a particle filter framework, whereas loop-closure detection is based on indexation and comparison of flow features. Both techniques are validated by performing off-line experimentation on real flow data captured in complex flow inside a model fish pass. The results highlight the potential of using flow sensing (in addition to conventional underwater sensing modalities of vision and sonar) for the tasks of underwater robot perception and localization.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Notes

  1. More on fish passes in Sect. 4.1.

  2. Microelectromechanical systems.

  3. KVS is a regular pattern of alternating vortices that are formed behind an object, when it is moved through still or flowing fluid.

  4. A video attachment for this experiment has also been supplied with the paper.

References

  • Akanyeti, O., Chambers, L., Jezov, J., Brown, J., Venturelli, R., Kruusmaa, M., et al. (2013). Self-motion effects on hydrodynamic pressure sensing: part I. forward–backward motion. Bioinsperation and Biomimetrics, 8, 1.

    Google Scholar 

  • Angeli, A., Filliat, D., Doncieux, S., & Meyer, J. A. (2008). Fast and incremental method for loop-closure detection using bags of visual words. IEEE Transactions on Robotics, 24(5), 1027–1037.

    Article  Google Scholar 

  • Carreras, M., Ridao, P., Garcia, R., & Nicosevici, T. (2003). Vision-based localization of an underwater robot in a structured environment. In IEEE international conference on robotics and automation, Taipei, Taiwan.

  • Cha, S. H. (2008). Taxonomy of nominal type histogram distance measures. In American conference on applied mathematics, world scientific and engineering academy and society (WSEAS), pp. 325–330.

  • Chen, L., Wang, S., McDonald-Maier, K., & Hu, H. (2013). Towards autonomous localization and mapping of AUVs: A survey. International Journal of Intelligent Unmanned Systems, 2, 97–120.

    Article  Google Scholar 

  • Chen, N., Tucker, C., Engel, J. M., Yang, Y., Pandya, S., & Liu, C. (2007). Design and characterization of artificial hair sensor for flow sensing with ultrahigh velocity and angular sensivity. Journal of Microelectromechanical Systems, 16, 999–1014.

    Article  Google Scholar 

  • Corke, P., Detweiler, C., Dunbabin, M., Hamilton, M., Rus, D., & Vasilescu, I. (2007). Experiments with underwater robot localization and tracking. In IEEE international conference on robotics and automation, Rome, Italy.

  • Dagamesh, A. M. K., Bruinink, C., Droodendink, H., Wiegerink, R. J., Lammerink, T. S. J., & Krijnen, G. J. M. (2010). Engineering of biomimetic hair-flow sensor arrays dedicated to high-resolution flow field measurements. In IEEE sensors.

  • DeVries, L., & Paley, D. (2013). Observability-based optimization for flow sensing and control of an underwater vehicle in a uniform flowfield. In American control conference, pp. 1386–1391.

  • DeVries, L., Lagor, F. D., Lei, H., Tan, X., & Paley, D. A. (2015). Distributed flow estimation and closed-loop control of an underwater vehicle with a multi-modal artificial lateral line. Bioinsperation and Biomimetrics, 10(2), 1.

    Google Scholar 

  • Eskinja, Z., Fabekovic, Z., & Vukic, Z. (2007). Localization of autonomous underwater vehicles by sonar image processing. In International symposium ELMAR, Zadar, Croatia.

  • Fernandez, V. I., Maertens, A., Yaul, F. M., Dahl, J., Lang, J. H., & Triantafyllou, M. S. (2011). Lateral-line-inspired sensor arrays for navigation and object identification. Marine Technology Society Journal, 45, 130–146.

    Article  Google Scholar 

  • Forouher, D., Hartmann, J., Litza, M., & Maehle, E. (2011). Sonar-based FastSLAM in an underwater environment using walls as features. In 15th international conference on advanced robotics, pp. 588–593.

  • Gonzalez-Barbosa, J. J. (2004). Vision panoramique pour la robotique mobile: Stéréovisoin et localisation par indexation d’images. Ph.D. thesis, Université Toulouse III.

  • Gonzalez-Barbosa, J. J., & Lacroix, S. (2002). Rover localization in natural environments by indexing panoramic images. In IEEE international conference on robotics and automation, Washington DC, USA.

  • Izadi, N., de Boer, M. J., Berenschot, J. W., & Krijnen, G. J. M. (2010). Fabrication of superficial neuromasts inspired capacitive flow sensors. Journal of Micromechanics and Microengineering, 20, 1–9.

    Article  Google Scholar 

  • Kamarainen, J. K., Kyrki, V., Ilonen, J., & Kalviainen, H. (2003). Improving similarity measures of histograms using smoothing projections. Pattern Recognition Letters, 24, 2009–2019.

    Article  Google Scholar 

  • Klein, A., & Bleckmann, H. (2011). Determination of object position, vortex shedding frequency and flow velocity using artificial lateral line canals. Beilstein Journal of Nanotechnology, 2, 276–283.

    Article  Google Scholar 

  • Kottapalli, A. G. P., Asadina, M., Miao, J. M., Barbastathis, G., & Triantafyllou, M. S. (2012). A flexible liquid crystal polymer MEMS pressure sensor array for fish-like underwater sensing. Smart Materials and Structures, 21, 1.

    Article  Google Scholar 

  • Lagor, F. D., DeVries, L. D., Waychoff, K. M., & Paley, D. A. (2013). Bio-inspired flow sensing and control: Autonomous underwater navigation using distributed pressure measurements. In 18th international symposium on unmanned untethered submersible technology, Portsmouth, NH, USA.

  • Lemaire, T. (2006). Simultaneous localisation and mapping with monocular vision. Ph.D. thesis, Ecole Nationale Supérieure de l’Aéronautique et de l’Espace, France.

  • Mallios, A., Ribas, D., & Ridao, P. (2009). Localization advances in the unstructured underwater environment. In Proceedings of the 9th Hellenic symposium of oceanography and fishery, pp. 111–116.

  • Mogdans, J., & Bleckmann, H. (2012). Coping with flow: Behavior, neurophysiology and modeling of the fish lateral line system. Biological Cybernetics, 106(11–12), 627–642.

    Article  Google Scholar 

  • Muhammad, N. (2008). Vision based simultaneous localisation and mapping for mobile robots. Masters Thesis, Université de Bourgogne, France.

  • Muhammad, N., & Lacroix, S. (2011). Loop closure detection using small-sized signatures from 3D lidar data. In IEEE international symposium on safety, security, and rescue robotics, Kyoto, Japan.

  • Muhammad, N., Strokina, N., Toming, G., Tuhtan, J., Kamarainen, J. K., & Kruusmaa, M. (2015). Flow feature extraction for underwater localization: preliminary results. In IEEE international conference on robotics and automation, Seattle, Washington, USA.

  • Muhammad, N., Toming, G., Tuhtan, J. A., Musall, M., & Kruusmaa, M. (2017). Underwater map-based localization using flow features. Autonomous Robots, 41, 417–436.

    Article  Google Scholar 

  • Palomeras, N., Nagappa, S., Ribas, D., Gracias, N., & Carreras, M. (2013). Vision-based localization and mapping system for AUV intervention. In MTS/IEEE OCEANS, Bergen.

  • Paull, L., Saeedi, S., Seto, M., & Li, H. (2014). AUV navigation and localization: A review. IEEE Jounal of Ocean Engineering, 39, 131–149.

    Article  Google Scholar 

  • Peleshanko, S., Julian, M. D., Ornatska, M., McConney, M. E., LeMieux, M. C., Chen, N., et al. (2007). Hydrogel-encapsulated microfabricated hair mimicking fish cupula neuromast. Advanced Materials, 19, 2903–2909.

    Article  Google Scholar 

  • Qualtieri, A., Rizzi, F., Epifani, G., Ernits, A., Kruusmaa, M., & Vittorio, M. D. (2012). Parylene-coated bioinspired artificial hair cell for liquid flow sensing. Microelectronic Engineering, 98, 516–519.

    Article  Google Scholar 

  • Salumae, T., & Kruusmaa, M. (2013). Flow-relative control of an underwater robot. Proceedings of the Royal Society A, 469, 1–19.

    Article  Google Scholar 

  • Silicon-Microstructures. (2013). SM5420C-030-A-P-S. 1701 McCarthy Blvd, Milpitas CA 95035, USA.

  • Thrun, S., & Montemerlo, M. (2006). The GraphSLAM algorithm with applications to large-scale mapping of urban structures. The International Journal of Robotics Research, 25(5–6), 403–429.

    Article  Google Scholar 

  • Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001). Robust monte carlo lozalization for mobile robots. Artiicial Intelligence, 128, 99–141.

    Article  MATH  Google Scholar 

  • Venturelli, R., Akanyeti, O., Visentin, F., Jezov, J., Chambers, L., Toming, G., et al. (2012). Hydrodynamic pressure sensing with an artificial lateral line in steady and unsteady flows. Bioinsperation and Biomimetrics, 7(3), 036,004.

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by Estonian Research Council grant IUT-339, and the BONUS FISHVIEW project which was supported by BONUS (Art 185), funded jointly by the EU,Keskkonnainvesteeringute Keskus (Estonia), Forschungszentrum Juellich Beteiligungsgesellschaft GmbH, and the German Federal Ministry for Education and Research (Germany), and the Academy of Finland.

Author information

Authors and Affiliations

  1. Center for Biorobotics, Tallinn University of Technology, Tallinn, Estonia

    Naveed Muhammad, Juan Francisco Fuentes-Perez, Jeffrey A. Tuhtan, Gert Toming & Maarja Kruusmaa

  2. Institute of Water and River Basin Management, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Mark Musall

Authors
  1. Naveed Muhammad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan Francisco Fuentes-Perez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeffrey A. Tuhtan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gert Toming
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mark Musall
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maarja Kruusmaa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Naveed Muhammad.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (avi 33401 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Muhammad, N., Fuentes-Perez, J.F., Tuhtan, J.A. et al. Map-based localization and loop-closure detection from a moving underwater platform using flow features. Auton Robot 43, 1419–1434 (2019). https://doi.org/10.1007/s10514-018-9797-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-018-9797-3

Keywords

Search

Navigation