基于点云去噪的球形标靶中心拟合研究

以球形标靶作为同名特征点进行点云数据配准时，若标靶附近有干扰物或扫描的标靶点云含有大量噪声，会对点云配准质量造成很大影响。针对目前点云配准对标靶自身噪声有所忽略的问题，分析球形标靶的特点，探讨小波阈值去噪方法的适用性，并对去噪小波基函数的选取进行实验，提出球形标靶点云离散噪声小波阈值去噪方法。实验表明，标靶自身点云的去噪不可忽视。分析结果表明，该方法能够更有效地滤除球面外围粗差噪声，单个球形标靶的中心位置拟合精度提高约0.8 mm，相较标靶球未经去噪的拼接结果，扫描物特征检查点的坐标拼接误差减小约5 mm，点云总体配准精度提升约20％，是一种有效的点云数据配准预处理方法。

Research on Spherical Target Center Location Based on Three-Dimensional Laser Scanning Point Cloud after De-Noising

When the spherical target is used as the feature point of the same name for point cloud data registration, if there is a large amount of noise in the vicinity of the target with interference or scanning target point cloud, it will have a great influence on the point cloud registration quality. In view of the current situation that the point cloud registration has neglected target self-scanning noise, the characteristics of spherical target are analyzed, the applicability of the wavelet threshold denoising method is discussed, and the method of selecting the wavelet base function is tested. A wavelet threshold denoising method of the spherical target point cloud discrete noise is proposed. Experimental results show that the denoising of the target's own point cloud cannot be neglected; the result of case analysis shows that the method can filter the rough noise near the sphere more effectively, the fitting accuracy of the center position of a single spherical target is increased by about 0.8 mm, as compared with the point cloud stitching result of the spherical target without denoising. The co-ordinate stitching distance error of the scan feature check point is reduced by about 5 mm, and the registration accuracy of the point cloud is increased by about 20%. It is an efficient preprocessing method for point cloud registration data, and can provide reference for the application of related engineering.