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TartanVO: A Generalizable Learning-based VO
[article]
2020
arXiv
pre-print
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.
arXiv:2011.00359v1
fatcat:niozq53upzfbjctn7sxe53n52i