结合SVM的激光雷达线特征提取算法

Algorithm of extracting line feature of laser radar combined with SVM

针对IEPF(iterative end point fit)算法提取激光雷达数据线特征过程中使用固定分割阀值导致的欠分割和过分割现象,提出一种结合支持向量机(support vector machine,SVM)的分开合并的线特征提取算法。在分开阶段,使用IEPF算法对数据初步分割;在合并阶段,调整阀值尽可能消除欠分割的线段,分别提取过分割的线段间和正常线段间的接近度、共线度、重叠度3个特征作为特征向量,训练SVM模型,将SVM模型应用于实际测试中,对于分类结果为过分割的线段执行合并。实验结果表明,该算法有效消除了绝大部分IEPF算法进行线段提取产生的过分割和欠分割线段。

Aiming at the phenomenon that the fixed segmentation threshold leads to under segmentation and over segmentation in extracting line feature of laser radar data using the IEPF algorithm,a separate and merged extraction algorithm of line feature combined with support vector machine was proposed.In separate stage,the IEPF algorithm was used to segment the data preliminarily.In the merged stage,the threshold value was adjusted to eliminate the line segments of under segmentation as far as possible,and the three features from over segmented line segments and normal line segments which included the adjacent degree,the collinear degree and the overlap degree were extracted respectively as feature vectors to train the SVM model.The SVM model was applied to the actual test,and the classification results which were over segmented line segments were merged.Experimental results show that the proposed algorithm effectively eliminates most of the over segmented line segments and under segmented line segments produced by line segment extraction using IEPF algorithm.