Point cloud target detection method combining spectral clustering and improved Euclidean clustering

Abstract

With the development of unmanned sensing technology, using lidar sensor to obtain 3D information of obstacles has become a research hotspot. Because the laser point cloud has the characteristics of near density and far sparse, it is easy to make the phenomenon of under segmentation of nearby objects and missing detection of distant objects in target detection, which is very easy to produce inaccurate detection results. In order to solve this problem, a combination of spectral clustering and improved European clustering algorithm is proposed, which effectively solves the problems of under segmentation and missing detection. In the experiment, the clustering effect of this algorithm is compared with the traditional European clustering algorithm and the latest point cloud detection algorithm. The real vehicle experimental results show that the average positive detection rate of the proposed algorithm is 86.9%, which is improved compared with other methods. The average time of the algorithm is 87ms, which is practical on the real vehicle platform.