한국해양공학회지 (Journal of Ocean Engineering and Technology)

  • 제38권5호
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  • Pages.210-217
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  • 2024
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  • 1225-0767(pISSN)
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  • 2287-6715(eISSN)
한국해양공학회 (Korean Society of Ocean Engineers)
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Development and Validation of a Point Cloud Data Processing Algorithm for Obstacle Recognition in Double Hull Block

  • Sol Ha (School of Mechanical and Ocean Engineering, Mokpo National University) ;
  • Namkug Ku (Department of Marine Design Convergence Engineering, Pukyong National University)
  • 투고 : 2024.07.26
  • 심사 : 2024.09.28
  • 발행 : 2024.10.31
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초록

Shipyards have recently been experiencing severe labor shortages, prompting increased adoption of production automation systems to mitigate this issue. This paper introduces a point cloud data acquisition and processing system designed to support automation operations within double-hull block environments. The acquisition system utilizes LiDAR sensors and is built as a portable device capable of conducting 360-degree scans inside double-hull blocks. The processing system integrates the RANSAC algorithm for plane recognition and a voxelization algorithm for object detection, enabling accurate identification of obstacles within the double-hull block. To validate the system, a full-scale test bench was constructed to replicate actual working conditions. Experimental results indicated that the system could detect the positions of various obstacles within the test bench with an accuracy of up to 100 mm, which is sufficient for the implementation of automation systems. The findings from this research are expected to facilitate the adoption of production automation in shipyards, enhancing productivity and addressing labor shortages in the industry.

키워드

과제정보

This work was supported by a Research Grant of Pukyong National University (2023).

참고문헌

  1. Besl, P. J., & McKay, N. D. (1992). A method for registration of 3-D shapes. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239-256. https://doi.org/10.1109/34.121791
  2. Dorai, C., Wang, G., Jain, A. K., & Mercer, C. (1998). Registration and integration of multiple object views for 3D model construction. In IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 83-89. https://doi.org/10.1109/34.655652
  3. Heo, S. H., Kang, M. J., Choi, J. W., & Park, J. H. (2024). Design of a mapping framework on image correction and point cloud data for spatial reconstruction of digital twin with an autonomous surface vehicle. Journal of the Society of Naval Architects of Korea, 61(3), 143-151. https://doi.org/10.3744/SNAK.2024.61.3.143
  4. Johnson, A. E., & Kang S. B. (1999). Registration and integration of textured 3D data. Image and Vision Computing, 17(2), 135-147. https://doi.org/10.1016/S0262-8856(98)00117-6
  5. Kim, J., Lee, H., & Cho, E. (December 2023), The present and future of smart yards and offshore plants., The Korean Society of Ocean Engineers News Letter, 10(3), 18-21.
  6. Lee, D. K., Ji, S. H., & Park, B. Y. (2021). Object detection and post-processing of LNGC CCS scaffolding system using 3D point cloud based on deep learning. Journal of the Society of Naval Architects of Korea, 58(5), 303-313. https://doi.org/10.3744/SNAK.2021.58.5.303
  7. Lee, D. K., Ji, S. H. & Park, B. Y. (2022a). PointNet and RandLA-Net algorithms for object detection using 3D point clouds. Journal of the Society of Naval Architects of Korea, 59(5), 330-337. https://doi.org/10.3744/SNAK.2022.59.5.330
  8. Lee, J. M., Lee, K .H., Kim, D. S., & Li, R. (2014). Point cloud data based augmented reality for rapid discrepancy check. Proceedings of the Society of CAD/CAM Engineers Conference, 225-228.
  9. Lee, J. W., Ku, N. K., & Lee, J. Y. (2022b), Development of point cloud data acquisition system prototype for sensing working environment in double hull block. Korean Journal of Computational Design and Engineering, 27(4), 375-381. https://doi.org/10.7315/CDE.2022.375
  10. Lee, J. W., Patil, A. K., Holi, P., & Chai, Y. H. (2016). A study on automatic modeling of pipelines connection using point cloud. Korean Journal of Computational Design and Engineering, 21(3), 341-352. https://doi.org/10.7315/CDE.2016.341
  11. Li, R., Lee, K. H., Lee, J. M., Nam, B. W., & Kim, D. S. (2016). A study on matching method of hull blocks based on point clouds for error prediction. Journal of the Computational Structural Engineering Institute of Korea, 29(2), 123-130. https://doi.org/10.7734/COSEIK.2016.29.2.123