控制与信息技术Issue(2)：103-110,8.DOI:10.13889/j.issn.2096-5427.2026.02.011
基于GPU并行运算的实时定位方法研究
Research on Real-Time Localization Method Based on GPU Parallel Computing
摘要
Abstract
To meet real-time and high-precision localization requirements in rail transit scenarios,a parallel computing localization method based on LiDAR and inertial measurement units(IMU)is proposed.Adopting a space-for-time design concept,this method utilizes the GPU characteristics:a large number of cores,strong parallel computing performance,and high video memory bandwidth.By constructing a voxel grid network,it enables efficient spatial indexing and nearest neighbor search of point clouds;voxel downsampling,LiDAR point cloud preprocessing,line and surface constraint construction,localization initialization,and relocation modules are transplanted to a graphics processing unit(GPU)for parallel operations.Experimental results show that the proposed method meets real-time localization requirements for automatic train operation from both localization accuracy and real-time performance,with an average decrease in CPU load rate by 25.66%,a 44.72%reduction in single-frame point cloud processing time from 85.87 ms to 47.47 ms,and an average absolute pose error of 0.10 m.关键词
轨道交通/实时定位/并行运算/图形处理器/体素栅格网络Key words
rail transit/real-time localization/parallel computing/GPU/voxel grid network分类
信息技术与安全科学引用本文复制引用
黄文宇,潘文波,李源征宇,陈志伟,杨振宇,袁超..基于GPU并行运算的实时定位方法研究[J].控制与信息技术,2026,(2):103-110,8.基金项目
湖南省自然科学基金项目(2025JJ50726) （2025JJ50726）
