Skip to main content

Advertisement

SpringerLink
Log in

Mapping and localization for autonomous ship using LiDAR SLAM on the sea

  • Original article
  • Published:
Journal of Marine Science and Technology Aims and scope Submit manuscript

Abstract

In this study, the accuracy of estimation of the ship’s position and the heading angle using simultaneous localization and mapping (SLAM) with a light detection and ranging (LiDAR) at sea was verified. In general, the ship’s position and the heading angle are obtained using a global navigation satellite system (GNSS) such as global positioning system (GPS) positioning and a GPS compass. The quasi-zenith satellite system (QZSS) has also emerged in Japan with the centimeter-level positioning augmentation system (CLAS), but it does not always provide the best positioning accuracy, even at sea. In addition, while position information and the heading angle of ship are important for control of maritime autonomous surface ships (MASS), they are conventionally dependent on GNSS-based sensor systems. In considering sensor redundancy for safety of MASS, this study performed SLAM using the LiDAR to create a point cloud map of the coast. The point cloud map was compared with open map data, and it was confirmed that a highly accurate map was obtained. This point cloud map can be used for autonomous navigation such as automatic berthing. In order to evaluate the accuracy of estimating the ship’s position and the heading angle using LiDAR SLAM, we used the experimental ship to simultaneously take measurements with the marine GPS compass and the QZSS receiver for comparison, in addition to the IMU and the LiDAR, and compared the measurement accuracy by the three sensors. As a result, the position and the heading angle were estimated with higher accuracy using LiDAR SLAM at \(\mu =\) 1.4 m (\(\sigma =\) 1.2 m) for position estimation than the GPS with the QZSS data in the RTK-Fix condition as a reference.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Data availability

The dataset referred to in the aforementioned article can be provided by the corresponding author on a reasonable request.

References

  1. Sawada R, Hirata K, Kitagawa Y, Saito E, Ueno M, Tanizawa K, Fukuto J (2021) Path following algorithm application to automatic berthing control. J Mar Sci Technol 26:541

    Article  Google Scholar 

  2. Office Cabinet (2022) Quasi-zenith satellite system performance standard (PS-QZSS-003). Tech Rep

  3. Office Cabinet (2022) Quasi-zenith satellite system interface specification centimeter level augmentation service (IS-QZSS-L6-004). Tech Rep

  4. Kato S, Tokunaga S, Maruyama Y, Maeda S, Hirabayashi M, Kitsukawa Y, Monrroy A, Ando T, Fujii Y, Azumi T (2018) Autoware on board: enabling autonomous vehicles with embedded systems. in 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS), pp. 287–296

  5. Elhousni M, Huang X (2020) A survey on 3D lidar localization for autonomous vehicles. in 2020 IEEE Intelligent Vehicles Symposium (IV), pp. 1879–1884

  6. Filipenko M, Afanasyev I (2018) Comparison of various slam systems for mobile robot in an indoor environment. In: 2018 International conference on Intelligent Systems (IS), pp 400–407

  7. Dang T, Tranzatto M., S. Khattak, F. Mascarich, K. Alexis, M. Hutter (2020) Graph-based subterranean exploration path planning using aerial and legged robots. J Field Robot 37(8):1363 (wiley Online Library)

  8. M. Kulkarni, M. Dharmadhikari, M. Tranzatto, S. Zimmermann, V. Reijgwart, P.D. Petris, H. Nguyen, N. Khedekar, C. Papachristos, L. Ott, R. Siegwart, M. Hutter, K. Alexis, Autonomous teamed exploration of subterranean environments using legged and aerial robots. in 2022 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, Philadelphia (PA), USA, 2022)

  9. Miki T, Wellhausen L, Grandia R, Jenelten F, Homberger T, Hutter M (2022) Elevation mapping for locomotion and navigation using GPU. https://doi.org/10.48550/ARXIV.2204.12876. https://arxiv.org/abs/2204.12876

  10. Grisetti G, Kümmerle R, Stachniss C, Burgard W (2010) A tutorial on graph-based slam. IEEE Intell Transp Syst Mag 2(4):31

    Article  Google Scholar 

  11. Ødven MS (2019) Lidar-based SLAM for autonomous ferry. Master’s thesis, Norwegian University of Science and Technology

  12. Kohlbrecher S, Meyer J, von Stryk O, Klingauf U (2011) A flexible and scalable slam system with full 3d motion estimation. in Proc. IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR) (IEEE)

  13. J. Zhang, S. Singh (2014) LOAM: lidar odometry and mapping in real-time. In: Fox D, Kavraki LE, Kurniawati H (eds) Robotics: Science and Systems X, University of California, Berkeley, USA, July 12-16, 2014

  14. Nelson E (2016) Berkeley localization and mapping. https://github.com/erik-nelson/blam. Accessed 29 June 2023

  15. T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, D. Rus, LIO-SAM: Tightly-coupled lidar inertial odometry via smoothing and mapping. in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020), pp. 5135–5142

  16. Zhou B, He Y, Qian K, Ma X, Li X (2021) S4-SLAM: A real-time 3D lidar SLAM system for ground/watersurface multi-scene outdoor applications. Auton Robot 45(1):77

    Article  Google Scholar 

  17. P. Biber, W. Strasser, The normal distributions transform: a new approach to laser scan matching. in Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), vol. 3 (2003), vol. 3, pp. 2743–2748 vol.3

  18. Rusu RB, Cousins S (2011) 3D is here: Point Cloud Library (PCL). in IEEE International Conference on Robotics and Automation (ICRA) (Shanghai, China, 2011)

  19. R. Kümmerle, G. Grisetti, H. Strasdat, K. Konolige, W. Burgard, g\(^2\)o: A general framework for graph optimization. in 2011 IEEE International Conference on Robotics and Automation (2011), pp. 3607–3613

  20. Besl P, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239

  21. Shan T, Englot B (2018) LeGO-LOAM: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2018), pp. 4758–4765

  22. Zhang J, Singh S (2017) Low-drift and real-time lidar odometry and mapping. Auton Robot 41(2):401

    Article  Google Scholar 

  23. Dellaert F, Kaess M (2017) Factor Graphs for Robot Perception (Now Publishers Inc.)

  24. Forster C, Carlone L, Dellaert F, Scaramuzza D (2015) IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation. In: Robotics: science and systems XI

    Book  Google Scholar 

  25. Dellaert F (2012) Factor graphs and GSTAM: a hands-on introduction. GT-RIM-CP&R-2012-002

  26. Sawada R, Hirata K (2021) Path planning for automatic berthing control with waterway geometry. In: Proceedings of Japan society naval architects and ocean engineers, pp. 37–40 (in Japanese)

  27. A. Carballo, J. Lambert, A. Monrroy, D. Wong, P. Narksri, Y. Kitsukawa, E. Takeuchi, S. Kato, K. Takeda, LIBRE: The multiple 3D lidar dataset. in 2020 IEEE Intelligent Vehicles Symposium (IV) (2020), pp. 1094–1101

Download references

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number 20K14971.

Author information

Authors and Affiliations

  1. National Maritime Research Institute, 6-38-1, Shinkawa, Mitaka, Tokyo, 181-0004, Japan

    Ryohei Sawada & Koichi Hirata

  2. Osaka University, 2-1 Yamadaoka, Suita, Osaka, Japan

    Ryohei Sawada

Authors
  1. Ryohei Sawada
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Koichi Hirata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ryohei Sawada.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sawada, R., Hirata, K. Mapping and localization for autonomous ship using LiDAR SLAM on the sea. J Mar Sci Technol 28, 410–421 (2023). https://doi.org/10.1007/s00773-023-00931-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00773-023-00931-y

Keywords

Search

Navigation