"基于点云切片改进法的不规则物体体积高精度测量研究"

cloud slicing improvement method for irregular object volume measurement.docx'. The existing boundary point sorting algorithms have limitations in distinguishing between multiple boundaries, generating abnormal boundary polygons, and calculating section areas incorrectly, leading to low accuracy in volume measurements. To address this issue, a point cloud slicing improvement method that considers segmentation of multiple contour boundaries in the section is proposed for high-precision measurement of irregular object volumes. This method first separates multiple boundaries in the slice by using Euclidean clustering or polygon splitting and recombining methods. Then, the PNPoly algorithm is used to determine the containment relationships between boundary polygons and calculate the section area. Finally, the volumetric measurement is obtained by accumulating the point cloud volumes in order of slicing. The effectiveness and accuracy of the two boundary segmentation methods proposed are compared and analyzed using multiple datasets, as well as the accuracy and efficiency of volume measurements. Experimental results demonstrate that the polygon splitting and recombining method has a high accuracy in boundary segmentation, strong applicability, and stable and reliable volume measurement accuracy with less computation time (relative errors in volume calculations for three datasets are 0.0901%, 0.0557%, and 0.0289%, with computation times of 2.229 s, 33.732 s, and 327.476 s, respectively), achieving the goal of high-precision volume measurement.