论文标题
塔尔坦沃：可推广的基于学习的VO
TartanVO: A Generalizable Learning-based VO
论文作者
论文摘要
我们提出了第一个基于学习的视觉探针学（VO）模型，该模型在挑战性的场景中概括了多个数据集和现实世界情景，并优于基于几何的方法。我们通过利用SLAM数据集塔塔尔（Tartanair）来实现这一目标，该数据集tartanair在具有挑战性的环境中提供了大量不同的合成数据。此外，为了使我们的VO模型跨数据集概括，我们提出了一个最新的损耗函数，并将摄像机内在参数纳入模型。实验表明，只能对合成数据进行训练的单个模型Tartanvo可以推广到Kitti和Euroc等现实世界中的数据集，从而证明了基于几何形状的挑战轨迹的方法具有显着优势。我们的代码可在https://github.com/castacks/tartanvo上找到。
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.
