Target Recognition Method of Street Lamp Based on Point Cloud Data

摘要

There are several problems need to be solved in the process of the recognition of the point cloud target in the urban traffic scene: Low target recognition accuracy due to noise and low real-time performance when dealing with a large amount of data. Therefore, in order to improve the situation, a new method, SVM target recognition algorithm based on fast density clustering is proposed in this paper. The workflow of the algorithm includes: (1) Point cloud denoising. In this step, most noise points are removed, the adverse effect of noise on subsequent processing is avoided. (2) A fast density clustering algorithm, GLDBSCAN, is used for clustering and then the eigenvectors are extracted. (3) A SVM classifier is used to identify the point cloud target. Experimental results show that the algorithm is effective. The algorithm presented in this paper is more accurate and efficient than the previous point cloud recognition algorithms.