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Camera, inertial measurement unit (IMU), and ultra-wideband (UWB) sensors are commonplace solutions to unmanned aerial vehicle (UAV) localization problems. The performance of a localization system can be improved by integrating observations from different sensors. In this paper, we propose a learning-based UAV localization method using the fusion of vision, IMU, and UWB sensors. Our model consists of visual&#x2013;inertial (VI) and UWB branches. We combine the estimation results of both branches to predict global poses. To evaluate our method, we augment a public VI dataset with UWB simulations and conduct a real-world experiment. The experimental results show that our method provides more robust and accurate results than VI/UWB-only localization. Our codes and data are available at <uri>https://imlabntu.github.io/VIUNet/</uri>.