一种地铁隧道退化环境下的地图构建方法

A Method for Map Construction in Degraded Environments of Subway Tunnels

为解决列车在地铁长隧道退化场景下的高精度点云地图构建需求,文章提出了一种基于激光雷达和惯性测量传感器的离线地图构建方法。该方法由基于误差卡尔曼滤波器的紧耦合前端里程计和基于因子图的后端优化组成。前端里程计利用惯性计算的结果进行预测,并根据激光雷达当前帧的点到局部地图平面点云的残差约束更新滤波器。其通过帧间里程计和其他约束因子构建全局位姿图,并进行平滑优化处理来构建地图。在轨道交通隧道环境下多次实验的结果显示,地图构建的位姿没有退化,多次数据的建图轨迹误差小于0.1 m,轨迹的一致性满足列车主动防撞的精度要求,这验证了该方法的有效性。

To meet the demand of high-precision point-cloud map construction under the scenes of degraded long subway tunnels,this paper proposes an offline map construction method based on LiDAR and inertial measurement sensors.The method consists of a tight coupling front-end odometry based on error-state Kalman filters and back-end optimization based on factor graphs.The front-end odometry predicts using the results of inertial computation and updates the filter based on the residual constraints of the current frame's points to the local map plane.The global pose graph is constructed based on inter-frame odometry and other constraint factors,followed by a smoothing optimization process to build the map.Results from multiple experiments in tunnel environments of rail transit demonstrate that the map construction maintains accurate poses without degradation.The trajectory errors of multiple data map construction are less than 0.1 meters,and the trajectory consistency meeting the precision requirements of active collision avoidance for trains.This validates the effectiveness of the proposed method.