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Fast and Accurate Edge Extraction Algorithm of Stacked Workpiece Point Cloud

Existing point cloud edge extraction algorithms extract the edge of stacked workpiece point cloud directly, however the extraction efficiency is low and the extraction effect is not as expected. Thereby in this paper, a kind of fast accurate point cloud stack workpiece edge extraction algorithm has been proposed to solve the point cloud edge extraction of stacked artifacts problems. Firstly, establishing a topological relationship of the point cloud of the stacked workpiece through the KD tree, calculating the k neighborhood of each point in the scene, and utilizing the principal component analysis method to calculate the normal. Moreover, the point cloud data is mapped to the Gaussian sphere, and the k-means clustering method which based on the Gaussian sphere mapping is proposed to cluster the point cloud data. Besides, the eigenvalues of the covariance matrix of the nearest neighbor points of each point of the clustering result are calculated and extract the edge of the point cloud. Eventually, experimental results show that the proposed approach could extract the edge of the point cloud quickly and accurately compared with other methods.