A Line Laser-Based Point Cloud Simplification Method

High-precision line laser scanning measurement technology has become an important means of surface inspection for key aerospace components. To improve detection efficiency, the million to ten million point clouds acquired by scanning must be simplified. Due to the complexity of the surface features (stepped planes, holes, etc.), the traditional point cloud simplification methods can hardly consider the simplification rate and the completeness of key features simultaneously. To meet the needs of matching point clouds with 3D models, this paper develops a point cloud simplification method that can preserve the boundaries of the point cloud based on Intrinsic Shape Signature (ISS) key points. Firstly, the point cloud boundary is extracted. Then, the feature points and the ISS key points are extracted. Finally, all point sets obtained are merged, and duplicate points are removed. This paper builds a 3D measurement system with a robot-mounted line laser scanner and a linear displacement stage. The scanning experiments are conducted using three types of brackets. Comparing the developed method in this paper with curvature-based grading methods and point by point forward methods, the results show that our method outperforms other methods