Enhanced Point Cloud Registration for Workpieces Using Triangular Sampling Constraints in Complex Industrial Environment

Accurately identifying and locating workpieces in complex industrial environment poses significant challenges due to high noise levels and large point cloud datasets. This paper proposes a method for acquiring workpiece point clouds using 3D structured light measurement for generating high-quality and reliable workpiece datasets. The proposed approach combines noise filtering, adaptive downsampling, PCA-FPFH feature extraction, and TCSAC (Triangle Sampling Consistency) to address the challenges posed by excessive noise and the substantial size of point cloud datasets, which significantly increase the computational overhead of registration algorithms. Additionally, the proposed method addresses the issue where most workpieces in industrial environment have similar features, which reduces the matching accuracy of point cloud registration. Experimental results demonstrate that our method improves both the accuracy and speed of point cloud registration in complex industrial environment and demonstrates strong performance on the 3DMatch open-source dataset. The relevant code is available at https://github.com/ZhentaoGuo/TCSAC.