지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.13 No.3
- 발행연도
- 2019.9
- 수록면
- 99 - 106 (8page)
- DOI
- 10.5626/JCSE.2019.13.3.99
이용수
초록· 키워드
오류제보하기
In this study, we have developed a point cloud segmentation algorithm for a collision avoidance system between cranes and other objects in construction yards. We used the Dynamic Graph CNN (DGCNN) algorithm to segment the point cloud of the entire yard into crane parts and backgrounds. The point cloud data were obtained from several LIDAR sensors attached to the crane. All points were grouped into specific core clusters using the DBSCAN algorithm. The core clusters were used to train the DGCNN after labeling with corresponding part names. This network classified the point cloud into crane types and their part names. Experimental results show that the crane part segmentation performance of the suggested algorithm is accurate enough to be used for collision avoidance system. It is possible to estimate the pose of a crane by comparing the segmented point clouds with those of the CAD model.
목차
Abstract
I. INTRODUCTION
II. RELATED WORK
III. LIDAR-BASED CRANE COLLISION AVOIDANCE SYSTEM
IV. CRANE PART SEGMENTATION ALGORITHM
V. EXPERIMENT
VI. CONCLUSION
REFERENCES
참고문헌 (8)
참고문헌 신청
H. Wu, Y. Yin, S. Wang, W. Shi, K. C. Clarke, and Z. Miao, “Optimizing GPS-guidance transit route for cable crane collision avoidance using artificial immune algorithm,” GPS Solutions, vol. 21, no. 2, pp. 823-834, 2017.
M. Ester, H. P. Kriegel, J. Sander, and X. Xu, “A densitybased algorithm for discovering clusters in large spatial databases with noise,” in Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, 1996, pp. 226-231.
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph CNN for learning on point clouds,” 2018; https://arxiv.org/abs/1801.07829.
E. Grilli, F. Menna, and F. Remondino, “A review of point clouds segmentation and classification algorithms,” The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, pp. 339-344, 2017.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: deep learning on point sets for 3D classification and segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, 2017, pp. 652- 660.
M. Ester, H. P. Kriegel, J. Sander, and X. Xu, “A densitybased algorithm for discovering clusters in large spatial databases with noise,” in Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, 1996, pp. 226-231.
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph CNN for learning on point clouds,” 2018; https://arxiv.org/abs/1801.07829.
E. Grilli, F. Menna, and F. Remondino, “A review of point clouds segmentation and classification algorithms,” The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, pp. 339-344, 2017.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: deep learning on point sets for 3D classification and segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, 2017, pp. 652- 660.
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