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AUV Bathymetric Simultaneous Localisation and Mapping Using Graph Method

Published online by Cambridge University Press:  05 July 2019

Teng Ma ,
Ye Li ,
Yusen Gong ,
Rupeng Wang ,
Mingwei Sheng  and
Qiang Zhang
Teng Ma*
Affiliation:
(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
Ye Li
Affiliation:
(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
Yusen Gong
Affiliation:
(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
Rupeng Wang
Affiliation:
(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
Mingwei Sheng
Affiliation:
(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
Qiang Zhang
Affiliation:
(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
*
(E-mail: liyeheu103@163.com)
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Abstract

Although topographic mapping missions and geological surveys carried out by Autonomous Underwater Vehicles (AUVs) are becoming increasingly prevalent, the lack of precise navigation in these scenarios still limits their application. This paper deals with the problems of long-term underwater navigation for AUVs and provides new mapping techniques by developing a Bathymetric Simultaneous Localisation And Mapping (BSLAM) method based on graph SLAM technology. To considerably reduce the calculation cost, the trajectory of the AUV is divided into various submaps based on Differences of Normals (DoN). Loop closures between submaps are obtained by terrain matching; meanwhile, maximum likelihood terrain estimation is also introduced to build weak data association within the submap. Assisted by one weight voting method for loop closures, the global and local trajectory corrections work together to provide an accurate navigation solution for AUVs with weak data association and inaccurate loop closures. The viability, accuracy and real-time performance of the proposed algorithm are verified with data collected onboard, including an 8 km planned track recorded at a speed of 4 knots in Qingdao, China.

Keywords

AUVsNavigationGraph SLAMTerrain estimationWeight voting method

Information

Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 6 , November 2019 , pp. 1602 - 1622
Copyright
Copyright © The Royal Institute of Navigation 2019 

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References

REFERENCES

Ånonsen, K. B., Hagen, O. K., Hegrenæs, Ø. and Hagen, P. (2013). The HUGIN AUV Terrain Navigation Module. MTS/IEEE OCEANS - San Diego, San Diego, CA, 1–8.Google Scholar
Barkby, S., Williams, S., Pizarro, O. and Jakuba, M. (2009). An efficient approach to bathymetric SLAM. IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, 219–224.Google Scholar
Barkby, S., Williams, S. B., Pizarro, O. and Jakuba, M. V. (2012). Bathymetric Particle Filter SLAM Using Trajectory Maps. International Journal of Robotics Research, 31(12), 1409–1430.Google Scholar
Chen, P. (2016). Study on Seabed Terrain Matching Navigation with Multi-Senor for AUV. Ph.D. dissertation, Harbin Engineering University. (in Chinese)Google Scholar
Dellaert, F. and Kaess, M. (2006). Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing. International Journal of Robotics Research, 25(12), 1181–1204.Google Scholar
Doble, M. J., Forrest, A. L., Wadhams, P. and Laval, B.E. (2009). Through-ice AUV Deployment: Operational and Technical Experience from Two Seasons of Arctic Fieldwork. Cold Regions Science & Technology, 56(2–3), 90–97.Google Scholar
Donovan, G. T. (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.Google Scholar
Durrant-whyte, H. and Bailey, T. (2006). Simultaneous localization and mapping: part I. IEEE Robotics & Automation Magazine, 13(3), 108–117.Google Scholar
Feng, Q. T. (2004). The Research on New Terrain Elevation Matching Approaches and Their Applicability. Ph.D. dissertation, Nation University of Defense Technology. (in Chinese)Google Scholar
Hagen, O. K. and Ånonsen, K. B. (2014). Using Terrain Navigation to Improve Marine Vessel Navigation Systems. Marine Technology Society Journal, 48(2), 45–58(14).Google Scholar
Hurtós, N., Ribas, D., Cufí, X., Petillot, Y. and Salvi, J. (2015). Fourier-based Registration for Robust Forward-looking Sonar Mosaicing in Low-visibility Underwater Environments. Journal of Field Robotics, 32(1), 123–151.Google Scholar
Ila, V., Polok, L., Solony, M. and Svoboda, P. (2017). SLAM++ -A highly efficient and temporally scalable incremental SLAM framework. International Journal of Robotics Research, 36(3), 210–230.Google Scholar
Johannsson, H., Kaess, M., Englot, B., Hover, F. and Leonard, J. (2010). Imaging sonar-aided navigation for autonomous underwater harbor surveillance. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan.Google Scholar
Kaess, M., Ranganathan, A. and Dellaert, F. (2008). iSAM: Incremental Smoothing and Mapping. IEEE Transactions on Robotics, 24(6), 1365–1378.Google Scholar
Kim, A. and Eustice, R. M. (2013). Real-Time Visual SLAM for Autonomous Underwater Hull Inspection Using Visual Saliency. IEEE Transactions on Robotics, 29(3), 719–733.Google Scholar
Kownacki, C. (2016). A concept of laser scanner designed to realize 3D obstacle avoidance for a fixed-wing UAV. Robotica, 34(2):243–257.Google Scholar
Li, Y., Ma, T., Wang, R., Chen, P. and Zhang, Q. (2017). Correction Method for AUV Seabed Terrain Mapping. Journal of Navigation, 70, 1062–1078.Google Scholar
Mallios, A., Ridao, P., Ribas, D. and Hernández, E. (2014). Scan Matching SLAM in Underwater Environments. Autonomous Robots, 36(3), 181–198.Google Scholar
Nygren, I. and Jansson, M. (2004). Terrain Navigation for Underwater Vehicles Using the Correlator Method. IEEE Journal of Oceanic Engineering, 29(3), 906–915.Google Scholar
Nygren, I. (2005). Terrain Navigation for Underwater Vehicles. Ph.D. dissertation, Royal Institute of Technology.Google Scholar
Palomer, A., Ridao, P., Ribas, D., Mallios, A., Gracias, N. and Vallicrosa, G. (2013). Bathymetry-based SLAM with Difference of Normals Point-cloud Subsampling and Probabilistic ICP Registration. MTS/IEEE OCEANS - Bergen, Bergen, Norway, 1–7.Google Scholar
Palomer, A., Ridao, P., Romagós, D. R. and Vallicrosa, G. (2015). Multi-beam Terrain/Object Classification for Underwater Navigation Correction. MTS/IEEE OCEANS – Genova, Genova, Spain, 1–5.Google Scholar
Palomer, A., Ridao, P. and Ribas, D. (2016). Multibeam 3D Underwater SLAM with Probabilistic Registration. Sensors, 16, 560.Google Scholar
Paull, L., Saeedi, S., Seto, M. and Li, H. (2014). AUV Navigation and Localization: A Review. IEEE Journal of Oceanic Engineering, 39, 131–149.Google Scholar
Ribas, D., Ridao, P., Tardós, J. D. and Neira, J. (2008). Underwater SLAM in Man-made Structured Environments. Journal of Field Robotics, 25 (11–12), 898–921.Google Scholar
Roman, C. and Singh, H. (2010). A Self-Consistent Bathymetric Mapping Algorithm. Journal of Field Robotics, 24 (1–2), 23–50.Google Scholar
Rosen, D. M., Kaess, M. and Leonard, J. J. (2012). An incremental trust-region method for Robust online sparse least-squares estimation, 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, 1262–1269.Google Scholar
Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic Robotics. MIT Press, Inc.Google Scholar
Stuckey, R. A. (2012). Navigational Error Reduction of Underwater Vehicles with Selective Bathymetric SLAM. Navigation Guidance & Control of Underwater Vehicles, 45(5), 118–125.Google Scholar
Wang, N., Lv, S., Zhang, W., Liu, Z. and Er, M. J. (2017a). Finite-time observer based accurate tracking control of a marine vehicle with complex unknowns. Ocean Engineering, 145, 406–415.Google Scholar
Wang, N., Su, S. F., Yin, J., Zheng, Z. and Meng, J. E. (2017b). Global Asymptotic Model-Free Trajectory-Independent Tracking Control of an Uncertain Marine Vehicle: An Adaptive Universe-Based Fuzzy Control Approach. IEEE Transactions on Fuzzy Systems, DOI:10.1109/TFUZZ.2017.2737405.Google Scholar
Zhou, L., Cheng, X., Zhu, Y., Dai, C. and Fu, J. (2017). An Effective Terrain Aided Navigation for Low-Cost Autonomous Underwater Vehicles. Sensors, 17, 680.Google Scholar