Simultaneous Localization of Rail Vehicles and Mapping of Surroundings With LiDAR-Inertial-GNSS Integration

Accurate rail vehicle positioning is crucial for railroad operational safety. Modern light detection and ranging (LiDAR) simultaneously localization and mapping (SLAM) systems have delivered excellent results in real-world scenarios. However, it still lacks well investigation for rail vehicle applic...

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Bibliographic Details
Published in:IEEE sensors journal 2022-07, Vol.22 (14), p.14501-14512
Main Authors: Wang, Yusheng, Lou, Yidong, Song, Weiwei, Tu, Zhiyong, Wang, Yapeng, Zhang, Shimin
Format: Article
Language:English
Subjects:
Freight traffic
Global navigation satellite system
Laser radar
Lidar
Localization
localization and mapping
Location awareness
Mapping
multi-sensor integration
Odometers
Pose estimation
Radar tracking
Rails
Railway tracks
Sensors
Simultaneous localization and mapping
Traffic speed
Train positioning
Vehicles
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Summary:Accurate rail vehicle positioning is crucial for railroad operational safety. Modern light detection and ranging (LiDAR) simultaneously localization and mapping (SLAM) systems have delivered excellent results in real-world scenarios. However, it still lacks well investigation for rail vehicle applications. In this paper, we propose to achieve real-time accurate and robust positioning and mapping for rail vehicles utilizing LiDAR SLAM. Our framework tightly couples one non-repetitive scanning LiDAR with IMU, wheel odometer, and global navigation satellite system (GNSS) into pose estimation and simultaneous global map generation. As frontend, the IMU/odometer preintegration data de-skews the denoised point clouds and produces initial guess for LiDAR odometry. Besides, we leverage the plane constraints from extracted rail tracks and the height descriptor to further improve the system accuracy. As backend, a sliding window based factor graph is constructed to jointly optimize multi-modal information. To ensure a globally-consistent and less blurry mapping result, we develop a two-stage mapping method to register the submaps to the global. The proposed method is extensively evaluated on real-world datasets of numerous scenarios, including general-speed and high-speed ones, both freight traffic and passenger traffic is covered. The results show that our system delivers meter-level localization accuracy even in large or degenerated environments.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3181264