| Summary: | 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–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>.
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