Traditional LiDAR systems encounter significant challenges with pose estimation accumulation errors during SLAM and require improvements in algorithm efficiency and real-time performance. This paper introduces a tightly coupled LiDAR-IMU integration method with enhancements in three key areas: Point cloud undistortion, feature extraction and registration, and tightly coupled pose estimation. To mitigate point cloud distortion caused by high-speed motion, a continuous time-domain motion correction method is proposed, along with a pre-fitting plane mechanism for feature extraction to balance accuracy and computational efficiency. To address the high computational cost of KNN search during feature matching, a tracking mechanism is introduced to reduce complexity. For improving state estimation accuracy, the method employs a framework optimized using a nonlinear geometric observer. Evaluation on public datasets demonstrates that the proposed method reduces trajectory APE by 30. 52% and 21. 36% compared to LIO-SAM and Fast-LIO, respectively. Additionally, computational efficiency improves by 59. 9% and 43. 7% , making the method highly effective for real-time applications.