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).
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