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We present RailLoMer in this article, to achieve real-time accurate androbust odometry and mapping for rail vehicles. RailLoMer receives measurementsfrom two LiDARs, an IMU, train odometer, and a global navigation satellitesystem (GNSS) receiver. As frontend, the estimated motion from IMU/odometerpreintegration de-skews the denoised point clouds and produces initial guessfor frame-to-frame LiDAR odometry. As backend, a sliding window based factorgraph is formulated to jointly optimize multi-modal information. In addition,we leverage the plane constraints from extracted rail tracks and the structureappearance descriptor to further improve the system robustness againstrepetitive structures. To ensure a globally-consistent and less blurry mappingresult, we develop a two-stage mapping method that first performs scan-to-mapin local scale, then utilizes the GNSS information to register the submaps. Theproposed method is extensively evaluated on datasets gathered for a long timerange over numerous scales and scenarios, and show that RailLoMer deliversdecimeter-grade localization accuracy even in large or degeneratedenvironments. We also integrate RailLoMer into an interactive train state andrailway monitoring system prototype design, which has already been deployed toan experimental freight traffic railroad.

RailLoMer: Rail Vehicle Localization and Mapping with LiDAR-IMU-Odometer-GNSS Data Fusion