Research on Simultaneous Localization and Intrusion Detection Methodology for Intelligent Trains Based on Multisource Fusion - ZENG Xiang; JIANG Guotao; LYU Yu; LI Cheng; PAN Wenbo; LUO Ziqi (Kongzhi Yu Xinxi Jishu)
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.