使用激光线结构光扫描仪得到机车走行部三维点云数据，实现了在三维数据中对螺栓进行自动识别和定位。使用关键点的快速点特征直方图(FPFH) 来描述点云特征，首先，将目标区域与预存螺栓模板进行特征匹配，并为目标区域的匹配点分配权重；然后，使用均匀的种子点在带权重的匹配点集中进行K-means 聚类，并删除点数过少的聚类簇；最后，使用Hough 变换的方法为经过筛选的聚类簇建立严格的分类器，判断出螺栓的有无和精确位置。实验证明了该方法的有效性。

The locomotive running gear 3D point cloud data are obtained by line-structured laser scanner, and the bolts on the locomotive running gear under the 3D point cloud data are recognized and located automatically. Firstly, fast point feature histograms (FPFHs) of the key points are calculated to describe the 3D features, and the target region is matched with the preselected bolt template. Then, K-means clustering is carried out on the weighted match point set using uniform seed points. Finally, the Hough transform method is used to establish a strict classifier for the clusters, and the existence and precise position of the bolts are determined. The experimental results verify the effectiveness of the proposed method.