A Fast Ground Segmentation Method of LiDAR Point Cloud From Coarse-to-Fine

Abstract

Light detection and ranging (LiDAR) has become an indispensable sensor for autonomous vehicles due to its unique properties. Segmentation of raw point cloud is a fundamental step in the perception processing pipeline, which is critical to the safe driving of autonomous vehicles. However, some existing methods are prone to over-segmentation, insufficient segmentation, or poor real-time performance in the process of point cloud segmentation. Therefore, we propose a coarse-to-fine segmentation strategy to balance segmentation accuracy and speed. The ground segmentation includes applying robust locally weighted regression (RLWR) to the nonuniformly divided bins of polar grids in concentric model to achieve coarse ground segmentation. The processing results are mapped to a range image, and the improved range image convolution method is used to refine the ground segmentation. We evaluate the proposed method on two public datasets, and the precision, recall, and harmonic mean of the ground segmentation are all above 93%, which is sufficient to demonstrate the robustness of our method. Using the segmentation strategy from coarse to fine, the average processing time per frame of the SemanticKITTI dataset and the Nagoya dataset are 39.49 and 42.63 ms, respectively, with an average of 41.06 ms, which can meet the real-time requirements of autonomous driving.