基于车载LiDAR的特征融合差分的车前道路提取方法  

Front-of-vehicle road extraction method based on feature fusion difference of vehicle LiDAR

作　　者：何光明[1] 韩士元[1,2] 陈月辉[1,2] 周劲[1,2] 杨君[3] HE Guangming;HAN Shiyuan;CHEN Yuehui;ZHOU Jin;YANG Jun(Shandong Provincial Key Laboratory of Network Based Intelligent Computing,University of Jinan,Jinan 250022,China;Institute of Artificial Intelligence,University of Jinan,Jinan 250022,China;School of Automotive Engineering,Shandong Jiaotong University,Jinan 250023,China)

机构地区：[1]济南大学山东省网络环境智能计算重点实验室,山东济南250022 [2]济南大学人工智能研究院,山东济南250022 [3]山东交通学院汽车工程学院,山东济南250023

出　　处：《测绘通报》2023年第12期13-18,共6页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金(62273164,62373164);山东省自然科学基金重点项目(ZR2020KF006)。

摘　　要：为应对行驶中变化的道路环境,划分当前车前道路的可行驶区域,本文提出一种基于多特征融合差分的车前道路检测方法。该算法通过形态学滤波法对原始点云进行地面点云提取,统计归纳地面点云数据,进而界定运算域,在运算域内划分不同纵深的差分元尺寸和起点,在差分元内进行特征参数的融合,形成特征矩阵,求解差分矩阵后再进行阈值滤波,进而实现车前道路点云的提取。本文首先与相关道路点云的提取算法进行对比,表明其性能优良;然后针对所采数据不同纵深的道路提取效果进行对比,证明该算法的有效性。In order to cope with the changing road environment during driving and divide the drivable area of the current road in front of the vehicle,this paper proposes a detection method for the road in front of the vehicle based on multi-feature fusion difference.This algorithm extracts the ground point cloud from the original point cloud by morphological filtering method,statistically summarizes the ground point cloud data to define the operation domain,divides the differential element size and starting point of different depths in the operation domain,fuses the characteristic parameters in the differential element,forms a feature matrix,solves the differential matrix,and performs threshold filtering,so as to realize the extraction of the point cloud in front of the vehicle.In this paper the extraction algorithm of the relevant road point cloud is compared to,which highlight its excellent performance and then the road extraction effect of different depths of the collected data is compared to prove the effectiveness of the algorithm.

关 键 词：车载LiDAR 点云 车前道路提取 特征融合 差分 

分 类 号：P237[天文地球—摄影测量与遥感]