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Communication-constrained cooperative bathymetric simultaneous localisation and mapping with efficient bathymetric data transmission method

Published online by Cambridge University Press:  04 April 2022

Teng Ma Open the ORCID record for Teng Ma [Opens in a new window] ,
Wenjun Zhang ,
Ye Li ,
Yuxin Zhao ,
Qiang Zhang ,
Xiaojun Mei  and
Jiajia Fan
Teng Ma
Affiliation:
Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, China. Laboratory of Science and Technology on Marine Navigation and Control, China State Shipbuilding Corporation, Tianjin, China
Wenjun Zhang*
Affiliation:
Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, China.
Ye Li
Affiliation:
Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, China.
Yuxin Zhao*
Affiliation:
College of Automation, Harbin Engineering University, Harbin, China.
Qiang Zhang
Affiliation:
Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, China.
Xiaojun Mei
Affiliation:
Laboratory of Robotics and Systems in Engineering and Science (LARSyS), ISR/IST, University of Lisbon, Lisbon, Portugal. College of Information Engineering, Shanghai Maritime University, China
Jiajia Fan
Affiliation:
Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, China.
*
*Corresponding author. E-mail: [email protected]; [email protected]
*Corresponding author. E-mail: [email protected]; [email protected]
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Abstract

Bathymetric simultaneous localisation and mapping (SLAM) methods yield accurate navigation results for autonomous underwater vehicles (AUVs) and can construct consistent seabed terrain maps. Multiple independently working vehicles can complete tasks like surveying and mapping efficiently, which means cooperative bathymetric SLAM using multiple AUVs is suitable for large-scale seabed mapping. However, the transmission of bathymetric measurements collected using a multi-beam echo sounder over a low bandwidth, noisy, and unreliable acoustic channel is difficult, making cooperative bathymetric SLAM very challenging. This paper develops a graph-based cooperative bathymetric SLAM system that can compress many bathymetric measurements into small-scale acoustic packets and yield accurate navigation results with a 10% loss of acoustic packets caused by unreliable acoustic communication. According to the simulation conducted using the field data, the new algorithm is shown to be robust and capable of providing accurate location and mapping results over a low bandwidth, noisy, and unreliable acoustic channel.

Keywords

communication-constrainedcooperative navigationbathymetric SLAMAUVgraph method
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 4 , July 2022 , pp. 1000 - 1016
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

Alves, B. A. da S. (2019). A navigation system for under-the-ice robotic operations. Master of Science thesis. University of Lisbon, Portugal.Google Scholar
Barkby, S., Williams, S. B., Pizarro, O. and Jakuba, M. V. (2012). Bathymetric particle filter SLAM using trajectory maps. The International Journal of Robotics Research, 31(12), 1409–1430.Google Scholar
Bonin-Font, F. and Burguera, A. (2020). Towards multi-robot visual graph-SLAM for autonomous marine vehicles. Journal of Marine Science and Engineering, 8(6), 437.CrossRefGoogle Scholar
Brown, H., Kim, A. and Eustice, R. M. (2008). Development of a Multi-AUV SLAM Testbed at the University of Michigan. OCEANS 2008, Quebec City, QC, Canada. IEEE, 1–6. DOI: 10.1109/OCEANS.2008.5151880CrossRefGoogle Scholar
Casalino, A. T. G., Simetti, E., Sperindè, A. and Torelli, S. (2014). Impact of LBL calibration on the accuracy of underwater localization. IFAC Proceedings Volumes, 47(3), 3376–3381.CrossRefGoogle Scholar
Choudhary, S., Carlone, L., Nieto, C., Rogers, J., Christensen, H. I. and Dellaert, F. (2017). Distributed mapping with privacy and communication constraints: Lightweight algorithms and object-based models. The International Journal of Robotics Research, 36(12), 1286–1311.CrossRefGoogle Scholar
Demim, F., Nemra, A., Louadj, K., Hamerlain, M. and Bazoula, A. (2018). An adaptive SVSF-SLAM algorithm to improve the success and solving the UGVs cooperation problem. Journal of Experimental & Theoretical Artificial Intelligence, 30(3), 389–414.Google Scholar
Deschaud, J. E. (2018). IMLS-SLAM: Scan-to-Model Matching Based on 3D Data. 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 2480–2485.CrossRefGoogle Scholar
Jang, J and Kim, J. (2019). Dynamic Grid Adaptation for Panel-Based Bathymetric SLAM. 2019 IEEE Underwater Technology (UT), IEEE, 1–4.CrossRefGoogle 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.CrossRefGoogle Scholar
Kim, J. and Jung, H. S. (2011). An Approach Towards Online Bathymetric SLAM. OCEANS’11 MTS/IEEE KONA, IEEE, 1–6. DOI: 10.23919/OCEANS.2011.6106951.Google Scholar
López, E., Garcia, S., Barea, R., Bergasa, L. M., Molinos, E. J., Arroyo, R., Romera, E. and Pardo, S. (2017). A multi-sensorial simultaneous localization and mapping (SLAM) system for low-cost micro aerial vehicles in GPS-denied environments. Sensors, 17(4), 802.CrossRefGoogle ScholarPubMed
Lu, F. and Milios, E. (1997). Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4(4), 333–349.CrossRefGoogle Scholar
Ma, T., Li, Y., Zhao, Y., Jiang, Y. and Pascoal, A. M. (2020a). Efficient bathymetric SLAM with invalid loop closure identification. IEEE/ASME Transactions on Mechatronics, 26(5), 2570–2580.Google Scholar
Ma, T. , Li, Y., Zhao, Y. X., Zhang, Q. and Zhang, Q. Y. (2020b). Robust bathymetric SLAM algorithm considering invalid loop closures. Applied Ocean Research, 102, 102298.CrossRefGoogle Scholar
Mandić, F., Rendulić, I., Mišković, N. and Nađ, Đ. (2016). Underwater object tracking using sonar and USBL measurements. Journal of Sensors, 8070286. doi:10.1155/2016/8070286Google Scholar
Mangelson, J. G., Vasudevan, R. and Eustice, R. M. (2018). Communication Constrained Trajectory Alignment for Multi-Agent Inspection via Linear Programming. OCEANS 2018 MTS/IEEE Charleston, SC, USA. IEEE, 1–8. DOI: 10.1109/OCEANS.2018.8604775.CrossRefGoogle Scholar
Marchel, Ł., Naus, K. and Specht, M. (2020a). Optimisation of the position of navigational aids for the purposes of SLAM technology for accuracy of vessel positioning. Journal of Navigation, 73(2), 282–295.CrossRefGoogle Scholar
Marchel, Ł., Specht, C., and Specht, M. (2020b). Testing the accuracy of the modified ICP algorithm with multimodal weighting factors. Energies, 13, 5939.CrossRefGoogle Scholar
Massot-Campos, M., Oliver-Codina, G. and Thornton, B. (2019). Laser Stripe Bathymetry Using Particle Filter SLAM. OCEANS 2019, Marseille, France. IEEE, 1–7.CrossRefGoogle Scholar
Norgren, P. and Skjetne, R. (2018). A multibeam-based SLAM algorithm for iceberg mapping using AUVs. IEEE Access, 6, 26318–26337.CrossRefGoogle Scholar
Ouyang, X., Zeng, F., Lv, D., Tianbao, D. and Haibing, W. (2020). Cooperative navigation of UAVs in GNSS-denied area with colored RSSI measurements. IEEE Sensors Journal, 21(2), 2194–2210.CrossRefGoogle Scholar
Palomer, A., Ridao, P., Ribas, D., Mallios, A. and Vallicrosa, G. (2014). Octree-Based Subsampling Criteria for Bathymetric SLAM. XXXV Jornadas de Automática, September 2014, Valencia, Spain, 3–5.Google Scholar
Palomer, A., Ridao, P. and Ribas, D. (2016). Multibeam 3D underwater SLAM with probabilistic registration. Sensors, 16(4), 560.CrossRefGoogle ScholarPubMed
Paull, L., Saeedi, S., Seto, M. and Li, H. (2013). AUV navigation and localization: A review. IEEE Journal of Oceanic Engineering, 39(1), 131–149.CrossRefGoogle Scholar
Paull, L., Huang, G., Seto, M. and Leonard, J. J. (2015). Communication-Constrained Multi-AUV Cooperative SLAM. 2015 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 509–516.CrossRefGoogle Scholar
Torroba, I., Bore, N. and Folkesson, J. (2019). Towards Autonomous Industrial-Scale Bathymetric Surveying. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, Macau, China, 6377–6382.CrossRefGoogle Scholar
Torroba, I., Sprague, C. I., Bore, N. and Folkesson, J. (2020). PointNetKL: Deep inference for GICP covariance estimation in bathymetric SLAM. IEEE Robotics and Automation Letters, 5(3), 4078–4085.CrossRefGoogle Scholar
Trujillo, J. C., Munguia, R., Guerra, E. and Grau, A. (2018). Cooperative monocular-based SLAM for multi-UAV systems in GPS-denied environments. Sensors, 18(5), 1351.CrossRefGoogle ScholarPubMed
Yuan, X., Martínezortega, J. F., Jas, F. and Eckert, M. (2017). AEKF-SLAM: A new algorithm for robotic underwater navigation. Sensors, 17(5), 1174.CrossRefGoogle ScholarPubMed