  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, encoder, and global
navigation satellite system (GNSS) 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 real-time kinematic (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.
