Abstract
We present herein a three-dimensional (3D) mapping method in one-way rectilinear scanning with an autonomous underwater vehicle (AUV) equipped with a forward looking sonar (FLS) and a profiling sonar (PS). Three-dimensional reconstruction using sonar with a finite beam width is an ill-posed problem, and additional constraints also need to be considered. Our approach involves an additional sonar and fuse acoustic measurements provided by the two sonar sensors. The FLS has a high resolution in the horizontal scan but has a uncertainty in the vertical scan. Meanwhile, the PS provides a reliable vertical profile, but its beam width is extremely narrow. An initial map is generated by the FLS and refined by combining the PS vertical scan data. To demonstrate the validity and effectiveness of the proposed method, we conducted tests in a water tank and also at sea. Finally, we presented the results of the proposed method gathered by an AUV in the tests.
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References
Arras, K. O., & Siegwart, R. Y. (1998). Feature extraction and scene interpretation for map-based navigation and map building. In: Mobile robots 12 (Vol. 3210, pp. 42-53). International Society for Optics and Photonics.
Aykin, M. D., & Negahdaripour, S. (2016). Three-dimensional target reconstruction from multiple 2-d forward-scan sonar views by space carving. IEEE Journal of Oceanic Engineering, 42(3), 574–589.
Belcher, E., Hanot, W., & Burch, J. (2002). Dual-frequency identification sonar (DIDSON). In: Proceedings of the 2002 interntional symposium on underwater technology (Cat. No. 02EX556) (pp. 187–192). IEEE.
Burguera, A. (2017). A novel approach to register sonar data for underwater robot localization. In: 2017 Intelligent systems conference (IntelliSys) (pp. 1034–1043). IEEE.
Cho, H., Kim, B., & Yu, S. C. (2017). AUV-based underwater 3-D point cloud generation using acoustic lens-based multibeam sonar. IEEE Journal of Oceanic Engineering, 43(4), 856–872.
Gerogiannis, D., Nikou, C., & Likas, A. (2011). A split-and-merge framework for 2D shape summarization. In: 2011 7th international symposium on Image and Signal Processing and Analysis (ISPA) (pp. 206–211). IEEE.
Guerneve, T., Subr, K., & Petillot, Y. (2018). Three-dimensional reconstruction of underwater objects using wide-aperture imaging SONAR. Journal of Field Robotics, 35(6), 890–905.
Hernàndez Bes, E., Ridao Rodríguez, P., Ribas Romagós, D., Batlle, I., & Grabulosa, J. (2009). MSISpIC: A probabilistic scan matching algorithm using a mechanical scanned imaging sonar. Journal of physical agents, 3(1), 3–11.
Hurtós, N. V. (2014). Forward-looking sonar mosaicing for underwater environments (Doctoral dissertation, Ph. D. Thesis, Universitat de Girona).
Joe, H., Kim, M., & Yu, S. C. (2014). Second-order sliding-mode controller for autonomous underwater vehicle in the presence of unknown disturbances. Nonlinear Dynamics, 78(1), 183–196.
Kim, K., Intrator, N., & Neretti, N. (2004). Image registration and mosaicing of acoustic camera images. In: Proceedings of the 4th IASTED international conference on visualization, imaging, and image processing (pp. 713–718).
Massot-Campos, M., Oliver, G., Bodenmann, A., & Thornton, B. (2016). Submap bathymetric SLAM using structured light in underwater environments. In: 2016 IEEE/OES Autonomous Underwater Vehicles (AUV) (pp. 181-188). IEEE.
Pyo, J., Cho, H., Joe, H., Ura, T., & Yu, S. C. (2015). Development of hovering type AUV “Cyclops’’ and its performance evaluation using image mosaicing. Ocean Engineering, 109, 517–530.
Ribas, D., Ridao, P., Neira, J., & Tardos, J. D. (2006). SLAM using an imaging sonar for partially structured underwater environments. In: 2006 IEEE/RSJ international conference on intelligent robots and systems (pp. 5040–5045). IEEE.
Ribas, D., Ridao, P., & Neira, J. (2010). Underwater SLAM for structured environments using an imaging sonar. Cham: Springer.
Teixeira, P. V., Kaess, M., Hover, F. S., & Leonard, J. J. (2016). Underwater inspection using sonar-based volumetric submaps. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4288–4295). IEEE.
VanMiddlesworth, M., Kaess, M., Hover, F., & Leonard, J. J. (2015). Mapping 3d underwater environments with smoothed submaps. Field and service robotics (pp. 17–30). Cham: Springer.
Walter, M. R. (2008). Sparse Bayesian information filters for localization and mapping (No. MIT/WHOI-2008-12). Woods Hole Oceanographic Institution MA Department of Ocean Engineering.
Walter, M., Hover, F., & Leonard, J. (2008). SLAM for ship hull inspection using exactly sparse extended information filters. In: 2008 IEEE international conference on robotics and automation (pp. 1463–1470). IEEE.
Acknowledgements
This research was a part of the project titled ’Gyeongbuk Sea Grant’, funded by the Ministry of Oceans and Fisheries, Korea.
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Joe, H., Cho, H., Sung, M. et al. Sensor fusion of two sonar devices for underwater 3D mapping with an AUV. Auton Robot 45, 543–560 (2021). https://doi.org/10.1007/s10514-021-09986-5
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DOI: https://doi.org/10.1007/s10514-021-09986-5