arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2011.00359cs.CVcs.LGcs.RO
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TartanVO:一个可推广的基于学习的VO

TartanVO: A Generalizable Learning-based VO

Carnegie Mellon University(卡内基梅隆大学)

Wenshan Wang, Yaoyu Hu, Sebastian Scherer

摘要:

我们提出了第一个基于学习的视觉里程计(VO)模型,该模型适用于多个数据集和真实世界的场景,在具有挑战性的场景中优于基于几何的方法。我们通过利用SLAM数据集TartanAir来实现这一目标,该数据集在具有挑战性的环境中提供了大量多样化的合成数据。此外,为了使我们的VO模型在数据集上推广,我们提出了一个最大规模的损失函数,并将相机的内部参数到模型中。实验表明,一个单一的模型,TartanVO,只训练合成数据,没有任何微调,可以推广到现实世界的数据集,如KITTI和EuRoC,表现出显着的优势,在具有挑战性的轨迹基于几何的方法。我们的代码可在https://github.com/castacks/tartanvo上获得。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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