TartanVO：汎化可能な学習ベース視覚オドメトリ
TartanVO: A Generalizable Learning-based VO
多様な合成データで学習し、カメラ内部パラメータとスケール不変損失を組み込むことで、実世界データセットにも汎化する学習ベース視覚オドメトリを実現した論文。
著者: Wenshan Wang, Yaoyu Hu, Sebastian Scherer
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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.
