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