  We present the first learning-based visual odometry (VO) model, which
generalizes to multiple datasets and real-world scenarios and outperforms
geometry-based methods in challenging scenes. We achieve this by leveraging the
SLAM dataset TartanAir, which provides a large amount of diverse synthetic data
in challenging environments. Furthermore, to make our VO model generalize
across datasets, we propose an up-to-scale loss function and incorporate the
camera intrinsic parameters into the model. Experiments show that a single
model, TartanVO, trained only on synthetic data, without any finetuning, can be
generalized to real-world datasets such as KITTI and EuRoC, demonstrating
significant advantages over the geometry-based methods on challenging
trajectories. Our code is available at https://github.com/castacks/tartanvo.
