Research on 3D Point Cloud De-Distortion Algorithm and Its Application on Euclidean Clustering

Abstract

The 3D point cloud data collected by 3D lidars have become a significant resource for autonomous vehicles to acquire road information. But these data tend to be inaccurate due to the turning or moving of autonomous vehicles while the lidar is working. Moreover, the traditional Euclidean clustering algorithm often causes false detection in the vicinity or missed detection in the distance if the Euclidean distance threshold is not selected properly. This paper proposes a method which contains three main steps to solve these problems: First, a de-distortion algorithm is applied to diminish the influence of distortion caused by the moving and turning of lidar; second, optimize the structure of the Euclidean clustering algorithm to make it run faster; and third, we applied an adaptive threshold of the Euclidean distance in the improved algorithm, so the improved algorithm is able to detect the relatively small objects in the distance while it can also detect the objects nearby without misjudgment. These features are all confirmed by the results of vehicle experiments which shows that with the help of proposed method, the distortion of point cloud reduced, the detection distance has increased by nearly 8 meters, and the clustering speed has also increased by 15%.