Ground extraction and road obstacle segmentation method based on point cloud data

Abstract

With the rise of artificial intelligence and other technologies, unmanned driving has become an important direction of future automobile development. The ground extraction of roads is a key task. The traditional method of ground extraction is limited to flat roads and the speed is slow. In view of the under-segmentation problem between the ground point cloud and the multi-objective object point cloud, we propose a ground segmentation algorithm for point cloud data based on multi-region segmentation. In this paper, the ground data can be accurately found by using the method of concentric region division for point cloud data. At the same time, it also has good robustness for uneven road surface, so as to provide a good foundation for the subsequent segmentation of road obstacles. For the extraction of road obstacles, we adopt the DBSCAN clustering method based on sub-region. According to the characteristics of near-density and distant point cloud, we adopt adaptive parameter selection for different regions to improve the accuracy of obstacle extraction. In order to verify the results, the use of KITTI data set on ubuntu18.04 using ROS system to complete the test, can maintain about 28fps processing speed.