Lang Hong, Peng Yuan, Zou Zheng, Zhu Shengxue, Peng Yichuan, Du Hao
The Key Laboratory for Traffic and Transportation Security of Jiangsu Province, Huaiyin Institute of Technology, Huaian 223003, China.
The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China.
Sensors (Basel). 2024 Oct 10;24(20):6544. doi: 10.3390/s24206544.
Road curb extraction is a critical component of road environment perception, being essential for calculating road geometry parameters and ensuring the safe navigation of autonomous vehicles. The existing research primarily focuses on extracting curbs from ordered point clouds, which are constrained by their structure of point cloud organization, making it difficult to apply them to unordered point cloud data and making them susceptible to interference from obstacles. To overcome these limitations, a multi-feature-filtering-based method for curb extraction from unordered point clouds is proposed. This method integrates several techniques, including the grid height difference, normal vectors, clustering, an alpha-shape algorithm based on point cloud density, and the MSAC (M-Estimate Sample Consensus) algorithm for multi-frame fitting. The multi-frame fitting approach addresses the limitations of traditional single-frame methods by fitting the curb contour every five frames, ensuring more accurate contour extraction while preserving local curb features. Based on our self-developed dataset and the Toronto dataset, these methods are integrated to create a robust filter capable of accurately identifying curbs in various complex scenarios. Optimal threshold values were determined through sensitivity analysis and applied to enhance curb extraction performance under diverse conditions. Experimental results demonstrate that the proposed method accurately and comprehensively extracts curb points in different road environments, proving its effectiveness and robustness. Specifically, the average curb segmentation precision, recall, and F1 score values across scenarios A, B (intersections), C (straight road), and scenarios D and E (curved roads and ghosting) are 0.9365, 0.782, and 0.8523, respectively.
路缘提取是道路环境感知的关键组成部分，对于计算道路几何参数和确保自动驾驶车辆的安全导航至关重要。现有研究主要集中于从有序点云中提取路缘，这受到点云组织结构的限制，难以应用于无序点云数据，并且容易受到障碍物干扰。为克服这些限制，提出了一种基于多特征过滤的无序点云路缘提取方法。该方法集成了多种技术，包括网格高度差、法向量、聚类、基于点云密度的alpha形状算法以及用于多帧拟合的MSAC（M估计样本一致性）算法。多帧拟合方法通过每五帧拟合路缘轮廓来解决传统单帧方法的局限性，在保留局部路缘特征的同时确保更准确的轮廓提取。基于我们自行开发的数据集和多伦多数据集，将这些方法集成起来创建一个强大的滤波器，能够在各种复杂场景中准确识别路缘。通过敏感性分析确定了最佳阈值，并应用于提高不同条件下路缘提取性能。实验结果表明，所提方法能在不同道路环境中准确、全面地提取路缘点，证明了其有效性和鲁棒性。具体而言，在场景A、B（十字路口）、C（直道）以及场景D和E（弯道和重影）中，路缘分割的平均精度、召回率和F1分数值分别为0.9365、0.782和0.8523。
