Odometry fusion is crucial for mobile robots with various types of sensors. However, traditional odometry fusion approaches often suffer from high complexity and limited robustness, particularly in complex environments with few features. To address these challenges, a loose coupling framework based on the UKF is introduced for odometry fusion. By integrating multiple odometry sources using the UKF, the framework enhances both accuracy and robustness in localization and mapping. The experimental results in indoor and outdoor scenes verified the effectiveness of the proposed method, which can not only improve the accuracy of localization and mapping but also can handle the situation if partial sensors are not functioning.