基于多源融合的机车障碍物检测系统研究OA

To enhance collision prevention capability in locomotive operating environments,this paper presents a multi-source fusion-based obstacle detection system for locomotives.Using high-precision track maps as prior information,this system combines real-time kinematic(RTK)high-precision positioning data with track authorization data from the signaling system to calculate forward track-centerline coordinates in the vehicle coordinate system during operation.A kinematic clearance is defined based on these track-centerline coordinates that are also projected onto the vision image to obtain 2D pixel coordinates of the track centerline.This kinematic clearance is used to segment LiDAR point clouds for obstacle detection,generating LiDAR-detected obstacles within the clearance.The 2D pixel coordinates of the track centerline are also used to select the target track from visually detected paths,allowing for subsequent collision-risk assessment of visually detected objects.Finally,fused LiDAR-vision detection results are output.Field applications demonstrate the system's capability of providing long-to medium-range obstacle warnings using vision-only detection and medium-to short-range collision prevention through fusion-based detection.