Abstract
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.
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