基于几何特征与三维点云特征的道路边沿识别算法

针对目前典型道路边沿识别算法存在实时性与可靠性难以兼顾的问题,基于多线激光雷达,根据道路边沿的几何特征与三维点云特征,提出了一种权衡实时性与可靠性的道路边沿识别算法。依据多线激光雷达扫描获取的大量点云数据,基于RANSAC算法的地面分割方法,滤除了预设感兴趣区域内的地面数据点,然后将剩余的无序点进行有序栅格化投射处理,根据道路边沿区域的几何特征与点云分布特征进行匹配筛选,再融合RANSAC的最小二乘法,以完成道路边沿曲线的鲁棒拟合。实验表明,算法在直道和弯道场景识别准确率均大于95%,耗时均低于15 ms,具有良好的准确性和实时性。所提算法能有效识别道路边沿,可为智能车可行驶区域的识别及控制提供理论参考与方法依据。

Curb recognition algorithm based on geometric characters and 3D point cloud features

Typical curb recognition algorithms have difficulty in balancing real-time performance and reliability. In this paper, with a multi-line LiDAR used, a curb recognition algorithm based on geometric features and 3D point cloud features of curb areas is proposed, which reaches a tradeoff between real-time performance and reliability. Faced with the large amount of point cloud data, the algorithm firstly proposes a ground segmentation method based on RANSAC algorithm, filtering out the ground points in the preset region of interest, and then the orderly rasterization of the remaining disordered points is carried out for matching and screening curb areas according to the curb''s geometric characters and the points'' distribution feature. After that, the least square method fused with RANSAC is proposed to achieve the robust fitting of curb curve. Experiments show that the recognition accuracy of the algorithm is more than 95% in both straight and bend scenes, and the time-consuming is less than 15 ms, which indicates the good accuracy and real-time performance of the proposed algorithm.The algorithm can effectively identify road curb, thus providing a theoretical reference and method basis for intelligent vehicle driving area recognition and its'' control.