汽车工程学报2026，Vol.16Issue(3)：381-394,14.DOI:10.3969/j.issn.2095-1469.2026.03.05
基于分块地图和点云配准的智能车辆定位方法
Intelligent Vehicle Localization Based on Block Maps and Point Cloud Registration
摘要
Abstract
To address the issue of low localization accuracy of intelligent vehicles due to satellite signal attenuation in complex urban driving scenarios,an intelligent vehicle localization method based on block maps and point cloud registration is proposed.Firstly,a tightly-coupled LiDAR-inertial odometry framework is applied to construct a high-precision map of the vehicle's driving environment,capturing prior environmental information of the scene.Then,a block map is generated by deploying a set of keyframes,and a dynamic sliding-window switching strategy is used for boundary management.Additionally,a branch-and-bound search strategy is introduced to determine the loading area of the prior map.The Scan Context descriptor is then utilized for matching to determine the initial pose of the vehicle within the regional map.Subsequently,the fast truncated least squares estimation and graduated non-convexity(GNC)point cloud registration algorithm are employed to iteratively solve for the precise pose based on the obtained initial pose.Finally,the proposed localization method was evaluated on both public datasets and real vehicle platforms.The test results in urban park scenarios demonstrate that the proposed global localization method achieves a good balance between time efficiency and accuracy,with a global localization error of 0.14 meters and a localization time of 0.76 second.Compared to existing localization algorithms,the maximum error is reduced by 31％,and the average lateral error of the vehicle during road driving is reduced to 0.15 meters.关键词
激光雷达/分块地图/点云配准/智能车辆定位Key words
LiDAR/block maps/point cloud registration/intelligent vehicle localization分类
交通工程引用本文复制引用
朱波,栗凯,谈东奎,姚明尧..基于分块地图和点云配准的智能车辆定位方法[J].汽车工程学报,2026,16(3):381-394,14.基金项目
安徽省自然科学基金(2208085QE153,2308085ME159) （2208085QE153,2308085ME159）
