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We propose an accurate and robust multi-modal sensor fusion framework,MetroLoc, towards one of the most extreme scenarios, the large-scale metrovehicle localization and mapping. MetroLoc is built atop an IMU-centric stateestimator that tightly couples light detection and ranging (LiDAR), visual, andinertial information with the convenience of loosely coupled methods. Theproposed framework is composed of three submodules: IMU odometry,LiDAR-inertial odometry (LIO), and Visual-inertial odometry (VIO). The IMU istreated as the primary sensor, which achieves the observations from LIO and VIOto constrain the accelerometer and gyroscope biases. Compared to previouspoint-only LIO methods, our approach leverages more geometry information byintroducing both line and plane features into motion estimation. The VIO alsoutilizes the environmental structure information by employing both lines andpoints. Our proposed method has been extensively tested in the long-duringmetro environments with a maintenance vehicle. Experimental results show thesystem more accurate and robust than the state-of-the-art approaches withreal-time performance. Besides, we develop a series of Virtual Reality (VR)applications towards efficient, economical, and interactive rail vehicle stateand trackside infrastructure monitoring, which has already been deployed to anoutdoor testing railroad.

MetroLoc: Metro Vehicle Mapping and Localization with LiDAR-Camera-Inertial Integration