  Odometry estimation is crucial for every autonomous system requiring
navigation in an unknown environment. In modern mobile robots, 3D
LiDAR-inertial systems are often used for this task. By fusing LiDAR scans and
IMU measurements, these systems can reduce the accumulated drift caused by
sequentially registering individual LiDAR scans and provide a robust pose
estimate. Although effective, LiDAR-inertial odometry systems require proper
parameter tuning to be deployed. In this paper, we propose LIO-EKF, a
tightly-coupled LiDAR-inertial odometry system based on point-to-point
registration and the classical extended Kalman filter scheme. We propose an
adaptive data association that considers the relative pose uncertainty, the map
discretization errors, and the LiDAR noise. In this way, we can substantially
reduce the parameters to tune for a given type of environment. The experimental
evaluation suggests that the proposed system performs on par with the
state-of-the-art LiDAR-inertial odometry pipelines but is significantly faster
in computing the odometry. The source code of our implementation is publicly
available (https://github.com/YibinWu/LIO-EKF).
