  LiDAR-Inertial Odometry (LIO) is typically implemented using an
optimization-based approach, with the factor graph often being employed due to
its capability to seamlessly integrate residuals from both LiDAR and IMU
measurements. Conversely, a recent study has demonstrated that accurate LIO can
also be achieved using a loosely-coupled method. Inspired by this advancements,
we present a LIO method that leverages the recursive Bayes filter, solved via
the Extended Kalman Filter (EKF) - herein referred to as KLIO. Within KLIO,
prior and likelihood distributions are computed using IMU preintegration and
scan matching between LiDAR and local map point clouds, and the pose, velocity,
and IMU biases are updated through the EKF process. Through experiments with
the Newer College dataset, we demonstrate that KLIO achieves precise trajectory
tracking and mapping. Its accuracy is comparable to that of the
state-of-the-art methods in both tightly- and loosely-coupled methods.
