The Chongqing Industry Key Research and Development Project(cstc2018jszx-cyzdX0124); The Ministry of Education-China Mobile Research Fund Project (MCM20170203)
为了在多种道路环境下准确提取智能汽车前方道路路沿，提出一种基于三维激光雷达的路沿检测算法。该算法采用随机采样一致性算法(random sample consensus, RANSAC)快速分割出道路区域，滤除了大部分非地面数据，提高后续步骤的处理速度；提出一种基于无向图邻域关系的多特征、宽阈值、多层次路沿特征提取算法，通过构造多种路沿几何特征设置较宽阈值以提高路沿检测精度；采用双向扫描线搜索算法获取路沿候选点，根据路沿特征点密度和全局连续性的特点进行聚类分析并去除噪声，用二次曲线拟合道路路沿。结果表明，该算法能够在车辆、行人和障碍物遮挡的情况下有效识别结构化直、弯道路路沿，算法准确率均高于86%，且检测道路宽度误差小于0.19m，验证了算法的鲁棒性和准确性。
In order to accurately extract the road edge in front of the intelligent vehicle in a variety of road environments, an approach of road edge detection based on 3D LIDAR is proposed. The algorithm firstly uses the random sampling consistency algorithm to quickly segment the road area, filter out most of the non-ground data, and improve the processing speed of the subsequent steps. An extraction algorithm based on the unrelated graph neighborhood relationship is proposed, which is characterized by multi-feature, wide-threshold and multi-level curb feature, to improve the accuracy of the roadside detection by setting a variety of roadside geometric features and wider thresholds. Then the candidate points of road edge are obtained by bidirectional scanning line search algorithm. Clustering analysis and denoising are carried out according to the characteristics of density and global continuity of road edge. Finally, the road edge is fitted by quadratic curve.The results show that the algorithm can effectively identify the structural straight and curved road edges under the occlusion of vehicles, pedestrians and obstacles. The accuracy of the algorithm is higher than 86%, and the detection road width error is less than 0.19 m, which verifies the robustness and accuracy of the algorithm.