LiDAR-Inertial-Odometry (LIO) integrates LiDAR and IMU data, offering benefits such as independence from lighting conditions and the ability to directly obtain accurate depth measurements. However, LiDAR-based SLAM performance may deteriorate in environments with insufficient geometric constraints. To address this challenge, a LiDAR-IMU-UWB pose odometry system is proposed to correct long-distance positioning errors for robot navigation in perceptually degraded environments. The proposed system employs an iterative Kalman filter to tightly couple LiDAR and IMU measurements into a LiDAR-IMU factor, which is then optimized in the back-end factor graph. Upon reception of a UWB factor, the system evaluates and, if viable, integrates it with the LiDAR-IMU factor within the factor graph optimization. An event-triggered intermittent involvement of the UWB factor in optimization enhances computational efficiency. Experimental validation on public datasets and real corridor datasets demonstrates a significant improvement in accuracy over conventional LiDAR-IMU fusion methods, confirming the system’s effectiveness.