A New Density-Based Clustering Method Considering Spatial Distribution of Lidar Point Cloud for Object Detection of Autonomous Driving

Abstract

Lidar is a key sensor of autonomous driving systems, but the spatial distribution of its point cloud is uneven because of its scanning mechanism, which greatly degrades the clustering performance of the traditional density-based spatial clustering of application with noise (DSC). Considering the outline feature of detected objects for intelligent vehicles, a DSC-based adaptive clustering method (DAC) is proposed with the adoption of an elliptic neighborhood, which is designed according to the distribution properties of the point cloud. The parameters of the ellipse are adaptively adjusted with the location of the sample point to deal with the uniformity of points in different ranges. Furthermore, the dependence among different parameters of DAC is analyzed, and the parameters are numerically optimized with the KITTI dataset by considering comprehensive performance. To verify the effectiveness, a comparative experiment was conducted with a vehicle equipped with three IBEO LUX8 lidars on campus, and the results show that compared with DSC using a circular neighborhood, DAC has a better clustering performance and can notably reduce the rate of over-segmentation and under-segmentation.