A Probability Occupancy Grid Based Approach for Real-Time LiDAR Ground Segmentation

Abstract

Autonomous vehicles have been emerging over the past few years because of the sophisticated processing of data from different types of perception sensors, such as LiDAR, radar, and camera. Ground segmentation plays an important role in the sequence of data processing for environment perception tasks, as it can help to reduce the size of data to be processed and further decrease the overall computational time. However, the over-segmentation, under-segmentation, or slow-segmentation on non-flat surface usually occurs due to the characteristics of the 3D LIDAR data, such as occlusion in complex urban environment. To address these problems, in this paper, we proposed a probability occupancy grid-based approach for real-time ground segmentation by using a single LiDAR sensor. The effectiveness and robustness of our proposed method are evaluated and demonstrated by the real-time experiments that span different traffic scenarios from heavy traffic to light traffic.