JCSE, vol. 13, no. 3, pp.99-106, September, 2019

DOI: http://dx.doi.org/10.5626/JCSE.2019.13.3.99

Point Cloud Segmentation of Crane Parts Using Dynamic Graph CNN for Crane Collision Avoidance

Hyeonho Jeong, Hyosung Hong, Gyuha Park, Mooncheol Won, Mingyu Kim, and Hoyeong Yu
Department of Mechatronics Engineering, Chungnam National University, Daejeon, Korea FM Electronics, Daejeon, Korea

Abstract: 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.

Keyword: Crane; 3D point cloud; Segmentation; DBSCAN; Dynamic graph, CNN

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