控制与信息技术Issue(4)：59-66,8.DOI:10.13889/j.issn.2096-5427.2024.04.008
基于多源融合的智能列车同时定位与侵限检测方法研究
Research on Simultaneous Localization and Intrusion Detection Methodology for Intelligent Trains Based on Multisource Fusion
摘要
Abstract
To achieve self-localization and collision warning for intelligent trains,a novel methodology for simultaneous localization and intrusion detection based on multisource fusion is proposed. The initial integration of LiDAR,inertial measurement unit (IMU),and point-cloud map data facilitates the development of a least squares optimization model,and this model is used to determine train motion states based on the graph optimization theory,fulfilling the task of train localization. Distortion-less point-cloud data,train localization data,and prior map data are then fused to create real-time 3D clearances for train operation. Following this,object detection is performed within these clearances based on depth map segmentation and a point cloud clustering algorithm to acquire the positions and sizes of intrusion objects. Subsequently,a visual inspection technique is applied to classify objects from images. Finally,time synchronization is established between the LiDAR and visual data,based on train localization information and IMU data. This allows for the fusion of objects detected by LiDAR and those from visual detection within image planes,yielding the categories,locations,and sizes of those detected intrusion objects at the conclusion of the intrusion detection process. Experimental results demonstrated deviations of not more than 20 cm in the transverse localization of trains and detection accuracy of up to 97.91％ for buffer stops within clearance ranges. The proposed approach provides accurate and robust results of both train localization and intrusion detection.关键词
多源融合/智能列车/自主定位/侵限检测/实时3D限界Key words
multisource fusion/intelligent train/self-localization/intrusion detection/real-time 3D clearance分类
交通运输引用本文复制引用
曾祥,蒋国涛,吕宇,李程,潘文波,罗子麒..基于多源融合的智能列车同时定位与侵限检测方法研究[J].控制与信息技术,2024,(4):59-66,8.基金项目
国家重点研发计划项目(2022YFB4300400), （2022YFB4300400）
