  Multi-modal sensor integration has become a crucial prerequisite for the
real-world navigation systems. Recent studies have reported successful
deployment of such system in many fields. However, it is still challenging for
navigation tasks in mine scenes due to satellite signal dropouts, degraded
perception, and observation degeneracy. To solve this problem, we propose a
LiDAR-inertial odometry method in this paper, utilizing both Kalman filter and
graph optimization. The front-end consists of multiple parallel running
LiDAR-inertial odometries, where the laser points, IMU, and wheel odometer
information are tightly fused in an error-state Kalman filter. Instead of the
commonly used feature points, we employ surface elements for registration. The
back-end construct a pose graph and jointly optimize the pose estimation
results from inertial, LiDAR odometry, and global navigation satellite system
(GNSS). Since the vehicle has a long operation time inside the tunnel, the
largely accumulated drift may be not fully by the GNSS measurements. We hereby
leverage a loop closure based re-initialization process to achieve full
alignment. In addition, the system robustness is improved through handling data
loss, stream consistency, and estimation error. The experimental results show
that our system has a good tolerance to the long-period degeneracy with the
cooperation different LiDARs and surfel registration, achieving meter-level
accuracy even for tens of minutes running during GNSS dropouts.
