道路空间特征与测量距离相结合的LiDAR道路边界点提取算法

A road curb points extraction algorithm combined spatial features and measuring distance

针对现有方法在较稀疏的16线激光雷达数据中提取道路边界点准确度较低的问题,本文提出一种道路空间特征与测量距离相结合的道路边界点提取方法:采用随机采样一致性(RANSAC)算法进行预处理,快速剔除道路区域外点;判断同条激光线中点与点之间的水平连续性和垂直连续性,去除大部分道路表面点;根据道路边界点的测量模型,结合原始测量距离修正保留的道路边界点,初步剔除非道路边界点;通过判断起始于被保留点的两个水平向量的夹角是否大于一定阈值,进一步精确剔除非道路边界点。试验结果表明,本文方法相对于现有方法能够较准确获取道路边界点,同时满足无人驾驶汽车环境感知的实时性要求。

Extracting accurate road curb is a crucial task for driverless vehicles.However,existing road curb points extraction methods are not robust for sparse 16-ray LiDAR data.This paper presents a road curb points extraction algorithm that combines multiscale spatial features and measuring distance.The points outside the road areas are firstly removed by adopting the random sample consensus(RANSAC)algorithm,then most of the road surface points are removed by judging the horizontal and vertical continuity between points in the same laser beam.According to the measurement model of the road curb points,if the measured distance of the reserved points is within a reasonable distance and the angle between the two horizontal vectors starting with the point is larger than a certain threshold,the point will be identified as the road curb point.Experiments show that the road curb points extraction method proposed in this paper performs better than the other methods under 16-ray LiDAR dataset and meets the real-time requirements for environmental perception of driverless vehicles.