Optimization of point cloud preprocessing algorithm for equipped vehicles

In view of the fact that the point cloud 3D model will be interfered by environmental factors, measurement methods and other random factors in the process of data scanning and acquisition, there will be some invalid points, outliers and internal noise points. In this paper, a point cloud denoising method based on adaptive density clustering and statistical filtering is proposed to process vehicle point cloud data. which can effectively preserve vehicle features while obtaining optimal denoising effect. Compared with the existing point cloud noise processing algorithms, this algorithm can remove noise better, and has shorter time-consuming and good applicability.