In this letter, we present a global navigation satellite system (GNSS) aided LiDAR-visual-inertial scheme, RailLoMer-V, for accurate and robust rail vehicle localization and mapping. RailLoMer-V is formulated atop a factor graph and consists of two subsystems: an odometer assisted LiDAR-inertial system (OLIS) and an odometer integrated Visual-inertial system (OVIS). Both the subsystem exploits the typical geometry structure on the railroads. The plane constraints from extracted rail tracks are used to complement the rotation and vertical errors in OLIS. Besides, the line features and vanishing points are leveraged to constrain rotation drifts in OVIS. The proposed framework is extensively evaluated on datasets over 800 km, gathered for more than a year on both general-speed and high-speed railways, day and night. Taking advantage of the tightly-coupled integration of all measurements from individual sensors, our framework is accurate to long-during tasks and robust enough to grievously degenerated scenarios (railway tunnels). In addition, the real-time performance can be achieved with an onboard computer.

Modern light detection and ranging (LiDAR) simultaneously localization and mapping (SLAM) systems have delivered excellent results in real-world scenarios. However, the potential of LiDAR SLAM still lacks well investigation for rail vehicle applications. This paper proposes a SLAM method for rail vehicles in tunnel environments, which fully exploits the typical geometric feature structure in the tunnels. The system receives measurements from a LiDAR and an IMU. As frontend, the estimated motion variation from IMU measurement de-skews the denoised point clouds and produces initial guess for frame-to-frame LiDAR odometry. A degeneracy-aware feature selection is employed to select the most informative features. As backend, a factor graph is formulated to jointly optimize the multi-modal information. Besides, we leverage the plane constraints from extracted rail tracks and the pole-like features to further constrain the 6D state estimation. In addition, the real-time performance can be achieved with an onboard computer. Through extensive evaluation of datasets gathered over an extended time range in a railway scenario, it has been demonstrated that our proposed system delivers reliable localization accuracy even in long tunnel environments.

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.

With the ability of providing direct and accurate enough range measurements, light detection and ranging (LiDAR) is playing an essential role in localization and detection for autonomous vehicles. Since single LiDAR suffers from hardware failure and performance degradation intermittently, we present a multi-LiDAR integration scheme in this article. Our framework tightly couples multiple non-repetitive scanning LiDARs with inertial, wheel encoder, and real-time kinematic (RTK) into pose estimation and simultaneous global map generation. Primarily, we formulate a precise synchronization strategy to integrate isolated sensors, and the extracted feature points from separate LiDARs are merged into a single sweep. The fused scans are introduced to compute the scan-matching correspondences, which can be further refined by additional RTK measurements. Based thereupon, we construct a factor graph along with the inertial preintegration result, estimated ground constraints, and RTK data. For the purpose of maintaining a restricted number of poses for estimation, we deploy a keyframe based sliding-window optimization strategy in our system. The real-time performance is guaranteed with multi-threaded computation, and extensive experiments are conducted in challenging scenarios. Experimental results show that the utilization of multiple LiDARs boosts the system performance in both robustness and accuracy.

Precise and real-time rail vehicle localization as well as railway environment monitoring is crucial for railroad safety. In this letter, we propose a multi-LiDAR based simultaneous localization and mapping (SLAM) system for railway applications. Our approach starts with measurements preprocessing to denoise and synchronize multiple LiDAR inputs. Different frame-to-frame registration methods are used according to the LiDAR placement. In addition, we leverage the plane constraints from extracted rail tracks to improve the system accuracy. The local map is further aligned with global map utilizing absolute position measurements. Considering the unavoidable metal abrasion and screw loosening, online extrinsic refinement is awakened for long-during operation. The proposed method is extensively verified on datasets gathered over 3000 km. The results demonstrate that the proposed system achieves accurate and robust localization together with effective mapping for large-scale environments. Our system has already been applied to a freight traffic railroad for monitoring tasks.