基于主视图投影的列车障碍物侵限实时感知方法

A Real-time Train Perception Method for Obstacle Intrusion Based on Front View Projection

为提高轨道交通自动驾驶列车的障碍物感知能力,需增加列车对运行场景中障碍物侵限的感知能力。针对目前多传感融合算法对限界分析较少、实时性差、算力要求高的问题,文章提出一种使用相机和激光雷达作为传感器实时感知侵限障碍物的方法。该方法基于主视图(front view,FV)二维投影平面进行信息融合,通过离线联合标定获得投影矩阵,将激光雷达点云投影到FV平面,从相机图像中提取轨道计算限界;并使用点云预测修正传感器同步误差,根据投影的障碍物点云和限界做出侵限判断。通过在列车上安装传感器和感知系统进行了数据采集和实验验证,结果显示,本方法能够在低功耗嵌入式设备中运行,算法平均耗时16.2 ms;可以实时检测列车运行时前方障碍物是否侵限,并同时获得障碍物的位置和尺寸信息。

In order to improve the obstacle perception ability of automatic train operation in rail transit,it is necessary to increase the train's ability to perceive obstacle intrusion in the operational scenarios.Aiming at addressing the limitations in the commonly used multi-sensor fusion algorithm,which include inadequate clearance analysis,poor real-time performance and high computing power demands,this paper proposes a method to perceive encroachment obstacles in real time with cameras and LiDAR as sensors.The proposed method,which applies the information fusion approach based on the two-dimensional projection plane of the front view(FV),involves the establishment of a projection matrix through offline joint calibration,projection of the LiDAR point cloud onto the FV plane,and extraction of track images from cameras,to enable clearance calculation.By incorporating correction of sensor synchronization errors based on point cloud prediction,a judgment regarding track intrusion can be made,dependent on the projected obstacle point cloud and calculated clearance.Data collection and experimental verification were carried out using a train equipped with sensors and a perception system.The experimental results show the practicality of the proposed method on low-power embedded devices,achieving an average algorithm time of 16.2 ms.Moreover,the method is proven effective in real-time detection of track intrusion by obstacles ahead of trains,while simultaneously acquiring their locations and sizes.