FAST-LIO2 is a highly efficient direct LiDAR-inertial odometry framework built on top of ikd-Tree, a novel incremental kd-tree data structure. It addresses the fundamental challenge of real-time 3D state estimation in dynamic environments by tightly fusing LiDAR point clouds and IMU measurements through an iterated Kalman filter. The system is designed to operate onboard UAVs and ground robots, providing centimeter-level localization accuracy at well above sensor frame rates. The key innovation of FAST-LIO2 lies in its incremental kd-tree mapping backend, which enables direct point cloud registration without requiring explicit feature extraction. By incrementally updating the map structure as new points arrive, the system avoids the costly global map- rebuilding bottleneck of traditional dense approaches. This makesFAST-LIO2 particularly well-suited for high-rate LiDAR sensors such as the Livox Mid-360, where point densities vary dramatically across the field of view. From a systems perspective, the codebase is lightweight, well-structured, and extensively tested on both aerial and legged platforms. The repository includes ROS wrappers, launch files, and configuration examples for several popular LiDAR models (Livox, Ouster, Velodyne). The ikd-Tree component is maintained as a standalone library, making it reusable in other geometric pipelines beyond odometry. Real-world benchmarks demonstrate that FAST-LIO2 achieves state-of-the-art accuracy while running at over 100 Hz on a portable computation unit. The broader impact is significant for autonomous navigation: accurate real-time odometry is the foundation of localization, mapping, and loop closure. FAST-LIO2 is widely adopted in the UAV research community and serves as the default odometry backend for several well-known mapping systems (such as R3LIVE and FAST-LIVO). Its efficiency and robustness make it an excellent starting point for researchers entering the LiDAR-inertial SLAM space.