Cylinder Point Extraction in Industrial Scenario Point Clouds Using Multiscale Prospecting
Meng Du, Zhou Yang, Cheng Wang, Di Cao, Huachen Zhao, Sheng Nie · IEEE Transactions on Instrumentation and Measurement · 2024
Cylinder point extraction is a crucial prerequisite for applications of industrial scenarios, such as reverse engineering and clearance analysis, using laser point clouds. This task currently suffers from data missing, noise, and interference from the planar objects. The generalization capability for cylindrical objects with different radii is also required. Considering the cylindrical object as the continuously extended cylinder, a region growth algorithm using a multiscale prospecting (MSP) strategy is proposed in this article. First, the preprocess is conducted to subsample the point cloud and estimate each point normal. Second, the kernel computation is used to detect the potential cylinder patches as kernels with the multiscale cylinder prospecting. Then, the rough extraction is performed to compute the cylinder parameters with the iterative growth starting from kernels. Finally, the fine extraction labels the raw point cloud by assigning points to different cylinders under the length constraint. The real-world point clouds, which are collected from four industrial scenarios, the simulated point cloud, and an open dataset are employed to evaluate the proposed method’s performance. Experimental results demonstrate the effectiveness and robustness of the proposed method. The average precision, recall, and$F1$-score on the real-world dataset and open dataset are 0.879, 0.894, 0.885 and 0.931, 0.893, 0.909, respectively.