Enhanced Curve-Based Segmentation Method for Point Clouds of Curved and Irregular Structures

This paper proposes an improved method for model-based segmentation of curved and irregular mounded structures in 3D measurements. The proposed method divides the point cloud data into several levels according to the reasonable width calculated from the density of points, and then fits a curve model with 2D points to each level separately. The classification results of specific types are merged to obtain specific structural measurement data in 3D space. Experiments were conducted on the proposed method using the region growth algorithm (SRG) and the model-based segmentation method (MS) provided in the PCL library as the control group. The results show that the proposed method achieves higher accuracy with a mean intersection merge ratio (MloU) of more than 0.8238, which is at least 37.92% higher than SRG and MS. The proposed method is also faster with a time-consuming only 1/5 of SRG and 1/2 of MS. Therefore, the proposed method is an effective and efficient way to segment the measurement data of curved and irregular mounded structures in 3D measurements. The method proposed in this paper has also applied in the practical robotic grinding task, the root mean square error of the grinding amount is less than 2 mm, and good grinding results are achieved.grinding results are achieved.