AI 中文总结
针对复杂道路场景中地面分割难题，提出基于自适应同心区域模型的两阶段地面分割方法，构建模型确定扇区数量，粗、细分割阶段分别采用不同约束提取和细化地面点，实验证明该方法能适应点云分布特征，提高分割稳定性。
AI 中文摘要
地面分割是地面移动平台自主导航、环境感知和目标检测的基本前提。针对复杂道路场景中稀疏远距离点云、地面起伏和非地面结构干扰导致的地面点分割不足问题，本文提出一种基于自适应同心区域模型的两阶段地面分割方法。首先构建自适应同心区域模型以动态确定每个环中的扇区数量，形成点分布更平衡的局部区域。基于此模型开发两阶段分割方法，粗分割阶段引入最低高度种子约束和高度衰减加权建立加权主成分分析平面拟合模型提取地面候选点；细分割阶段采用反射强度一致性约束区分高置信度地面点和不确定点，并基于高置信度邻域的局部高度稳定性进一步细化不确定点。实验结果表明，该方法在SemanticKITTI数据集上的精度、召回率和F1分数分别为99.12%、96.24%和97.66%，在使用RUBY-PLUS采集的自采点云上分别为98.72%、100.00%和99.36%。结果表明该方法能有效适应激光雷达点云近密远疏的距离相关分布特征，减少非地面点误分类同时保持地面点召回率，有效提高地面分割稳定性。
英文摘要
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.
