Objects Detection With 3-D Roadside LIDAR Under Snowy Weather

Abstract

Rain and snowfall will increase noise, change the resolution of objects in the point cloud and present great challenges to the accurate recognition of traffic objects. Accordingly, this article proposes an efficient real-time method for roadside 3-D light detection and ranging (LIDAR) background point cloud extraction and object segmentation under snowy weather. We first use a historical point cloud sequence to quickly construct a background model, extract the background point cloud from the current frame by using a background difference method and update the background model in real-time. Then, the noise caused by snowfall in the non-background point cloud is filtered based on the beam density difference of the object point clouds. Finally, the remaining object point cloud is accurately segmented based on the proposed hierarchical object clustering method. We use an intelligent roadside system equipped with 3-D roadside LIDAR to collect point cloud data in a snowfall environment and evaluate the proposed method qualitatively and quantitatively. Experimental results show that the proposed method effectively avoids the problem of under-segmentation and over-segmentation of object point clouds under snowy conditions, and the precision and recall rate of traffic object segmentation reached 96.41% and 95.02%, respectively, thereby indicating a significant improvement in the accuracy and reliability of traffic object detection using roadside LIDAR under snowy conditions.