Metro-Rail-SLAM for Automated Inspection Vehicles with Path-Aided Constraints

Automated vehicles mounted on the rail are widely applied for metro infrastructure inspection and maintenance. However, the long-distance enclosed tunnel environment and severe feature degradation pose significant challenges to accurate vehicle localization. In this paper, we proposed a Metro-Rail-SLAM for automated inspection vehicles. The design path constraints are modeled as a path-aided likelihood model (PA-LM) by applying Kernel Density Estimation from the designed path of construction drawing. Besides, we detected emergency-bays from LiDAR as landmarks. The PA-LM and landmarks together with the odometry are integrated into a particle filter to develop a fast, accurate, and robust SLAM. The proposed models and method were validated with a prototyped inspection robot in an actual metro construction scenario located in Chengdu. Experimental results demonstrate that the proposed Metro-Rail-SLAM has good capability of localization and mapping.