为实现智能车辆对不同环境道路信息的有效提取,提出一种基于超体素分割的道路信息提取方法,道路信息主要分为路沿信息与车道线信息.首先,根据点云高程信息或扫描系统安装位置滤除非地面点云;然后,采用体素自适应的超体素分割方法对点云进行过分割,实现道路路沿特征的单独分割;接下来,通过边界点提取算法与扫描系统行驶轨迹完成路沿提取,并根据路沿信息划分出可行驶区域;最后,通过局部自适应阈值分割和空间密度滤波实现车道线提取.实验结果表明,道路路沿高度提取准确率为92.6%,道路宽度提取准确率为98.4%,车道线提取偏差度低于4%,最大偏差距离不大于0.04 m.
Road Information Extraction Method Based on Hypervoxel Segmentation
To effectively extract road information from different environments for intelligent vehicles,a road information extraction method based on hypervoxel segmentation is proposed.Road information is mainly divided into road edge and lane line information.First,the non-ground point cloud is filtered according to either the point cloud elevation information or installation location of scanning system.Second,the point cloud is over-segmented using the voxel adaptive hypervoxel segmentation method,allowing the separate segmentation of road edge features.Third,the boundary point extraction algorithm and driving path of scanning system are used to complete the extraction of road edges,subsequently dividing the driving area according to the road edge information.Finally,lane lines are extracted using local adaptive threshold segmentation and spatial density filtering.Experimental results show that the extraction accuracy of the road edge height and road width are 92.6%and 98.4%,deviation degree of lane line is less than 4%,and maximum deviation distance is no more than 0.04 m.
laser point cloudhypervoxel segmentationroad edgelane line
NETL
NSTL
万方数据
