为了保证工业机器人磨抛的加工质量，利用激光扫描技术对机器人夹持工件的形状误差以及装夹误差进行测量和评估，包括点云数据的获取和去噪。采用条纹式激光扫描仪配合直线匀速运动对机器人末端夹持工件进行扫描，通过调节测量和运动参数，获取近似网格点云。为了去除点云中存在的大尺度噪点，在K近邻均值滤波（KNNMF）算法基础上，提出了基于局部均值的K近邻均值滤波（LMKMF）算法对偏大的数据点进行局部预先滤波，并建立相关数学模型。以峰值信噪比作为评价标准，以实际测量点云样本为测试对象进行去噪测试。结果表明，相比标准的KNNMF算法，结合LMKMF预先滤波的KNNMF算法在30%噪点密度下去噪能力提升了53.78%，证实了其在高密度噪点下具有更好的去噪能力和特征保持能力。

For the quality of robot polishing process, the laser scanning technology is applied to the measurement and assessment of the shaping error and the clamping error of robot clamping workpiece, including the acquisition and denoising process of point cloud data. A stripe type laser scanner in uniform linear motion is used to scan a part clamped by robot. The approximate grid point cloud is obtained by adjusting the measurement and motion parameters. In order to remove the large scale noise in point cloud, the local mean K-nearest-neighbor mean filter (LMKMF) based on the K nearest neighbor mean filter(KNNMF) is proposed as local filter of partial large scale data point in advance. Relevant mathematical model is established. Peak signal-to-noise ratio is used as evaluation standard, and the actual measurement point cloud samples are used as the denoising test object. Results show that compared with the KNNMF, the denoising ability of the algorithm combining LMKMF with KNNMF has an improvement of 53.78% under the noise density of 30%, which proves that the proposed algorithm has greater ability of denoising and detail preserving when the noise density is high.