  LiDAR SLAM has become one of the major localization systems for ground
vehicles since LiDAR Odometry And Mapping (LOAM). Many extension works on LOAM
mainly leverage one specific constraint to improve the performance, e.g.,
information from on-board sensors such as loop closure and inertial state;
prior conditions such as ground level and motion dynamics. In many robotic
applications, these conditions are often known partially, hence a SLAM system
can be a comprehensive problem due to the existence of numerous constraints.
Therefore, we can achieve a better SLAM result by fusing them properly. In this
paper, we propose a hybrid LiDAR-inertial SLAM framework that leverages both
the on-board perception system and prior information such as motion dynamics to
improve localization performance. In particular, we consider the case for
ground vehicles, which are commonly used for autonomous driving and warehouse
logistics. We present a computationally efficient LiDAR-inertial odometry
method that directly parameterizes ground vehicle poses on SE(2). The
out-of-SE(2) motion perturbations are not neglected but incorporated into an
integrated noise term of a novel SE(2)-constraints model. For odometric
measurement processing, we propose a versatile, tightly coupled LiDAR-inertial
odometry to achieve better pose estimation than traditional LiDAR odometry.
Thorough experiments are performed to evaluate our proposed method's
performance in different scenarios, including localization for both indoor and
outdoor environments. The proposed method achieves superior performance in
accuracy and robustness.
