基于路侧激光雷达的障碍物目标检测方法

Obstacle target detection method based on roadside LiDAR

针对现有障碍物检测方法在复杂道路场景下存在地面分割欠精准、计算量大以及不同距离下的目标聚类困难问题,提出了一种基于路侧激光雷达的障碍物检测方法。在地平面分割方面,提出基于圆柱坐标系的改进扇形栅格模型以及最低点代表法优化种子点的选取,采用多地平面模型并通过随机采样一致性算法(RANSAC)实现地面拟合及分割。在障碍物聚类方面,构建KDTree加速聚类过程,提出划分区域及阈值自适应的方式改进欧氏聚类算法。实验结果表明,该方法在4种典型道路场景下对地面点的分割准确率均达到86%以上,且针对不同距离下的障碍物目标聚类准确率提升明显。

Aiming at the problems of the existing obstacle detection methods in complex road scenes,such as inaccurate ground segmentation,large amount of calculation and difficult target clustering at different distances,a roadside LiDAR based obstacle detection method was proposed.In the aspect of ground plane segmentation,the improved fan-shaped grid map based on cylindrical coordinate system and the lowest point representation method are proposed to optimize the selection of seed points.The ground fitting and segmentation are realized by using multi-plane model and random sample consensus algorithm(RANSAC).In the aspect of obstacle clustering,KDTree is constructed to accelerate the clustering process,and the Euclidean clustering algorithm is improved by dividing the region and threshold adaptive.The experimental results show that the segmentation accuracy of this method for ground points in four typical road scenes is more than 86%,and the clustering accuracy of obstacle targets at different distances is significantly improved.