Feature Extraction of Three-Point Cloud Images Based on Clustering Analysis

In this paper, a method of feature extraction in three-dimensional data obtained by laser line structure light is investigated and implemented, for extracting the feature of key parts of locomotive bottom and the locomotive bolt is taken as an example for experimental testing. We use the eigenvalues of the covariance matrix as the features to cluster a number of ribbons, and then use the ISS (Intrinsic Shape Signatures) -based method to get the key points of data in each cluster. The key point is projected to the local surface which is fitted by the least square method based on the key point to form a smooth feature line. The results show that this method is effective, and the feature extraction of 3D point cloud image based on cluster analysis is feasible in railway environment.