🤖 AI Summary
This study addresses the under-segmentation of ground points in sparse long-range point clouds, which is exacerbated by terrain undulations and interference from non-ground structures. To tackle this challenge, the authors propose a two-stage ground segmentation method based on an adaptive concentric zone model. In the coarse segmentation stage, dynamic sector partitioning balances local point density, and plane fitting is guided by a minimum-height seed constraint combined with height-decay weighting. The fine segmentation stage incorporates reflectance intensity consistency to select high-confidence ground points and refines ambiguous regions using neighborhood height stability. By innovatively integrating geometric and intensity features, the proposed approach achieves F1 scores of 97.66% on SemanticKITTI and 99.36% on RUBY-PLUS, significantly enhancing both segmentation accuracy and robustness.
📝 Abstract
Ground segmentation is a fundamental prerequisite for autonomous navigation, environmental perception, and object detection in ground mobile platforms. To address the under-segmentation of ground points caused by sparse long-range point clouds, ground undulations, and interference from non-ground structures in complex road scenarios, this paper proposes a two-stage ground segmentation method based on the Adaptive Concentric Zone Model. First, an Adaptive Concentric Zone Model is constructed to dynamically determine the number of sectors in each ring, thereby forming local zones with more balanced point distributions. Based on this model, a two-stage ground segmentation method is developed. In the coarse segmentation stage, a lowest-height seed constraint and height-decay weighting are introduced to establish a weighted principal component analysis plane fitting model, from which ground candidate points are extracted. In the fine segmentation stage, a reflectance intensity consistency constraint is employed to distinguish high-confidence ground points from uncertain points, and the uncertain points are further refined based on the local height stability of high-confidence neighborhoods. Experimental results show that the proposed method achieves Precision, Recall, and F1-score values of 99.12%, 96.24%, and 97.66% on the SemanticKITTI dataset, and 98.72%, 100.00%, and 99.36%, respectively, on a self-collected point cloud acquired using a RUBY-PLUS. The results demonstrate that the proposed method can effectively adapt to the range-dependent distribution characteristics of LiDAR point clouds, which are dense at near ranges and sparse at far ranges. It reduces the misclassification of non-ground points while maintaining ground point recall, thereby effectively improving the stability of ground segmentation.