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TartanVO: A Generalizable Learning-based VO

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Wenshan Wang (CMU)*; Yaoyu Hu (Carnegie Mellon University); Sebastian Scherer (Carnegie Mellon University)

Interactive Session

2020-11-18, 11:50 - 12:20 PST | PheedLoop Session


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


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Reviews & Rebuttal

Conference on Robot Learning 2020