Recently, deep learning frameworks have been deployed in visual odometry systems and achieved comparable results to traditional feature matching based systems. However, most deep learning-based frameworks inevitably need labeled data as ground truth for training. On the other hand, monocular odometry systems are incapable of restoring absolute scale. External or prior information has to be introduced for scale recovery. To solve these problems, we present a novel deep learning-based RGB-D visual odometry system. Our two main contributions are: (i) during network training and pose estimation, the depth images are fed into the network to form a dual-stream structure with the RGB images, and a dual-stream deep neural network is proposed. (ii) the system adopts an unsupervised end-to-end training method, thus the labor-intensive data labeling task is not required. We have tested our system on the KITTI dataset, and results show that the proposed RGB-D Visual Odometry (VO) system has obvious advantages over other state-of-the-art systems in terms of both translation and rotation errors.

中文翻译：

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基于无监督深度学习的 RGB-D 视觉里程计

最近，深度学习框架已部署在视觉里程计系统中，并取得了与基于特征匹配的传统系统相当的结果。然而，大多数基于深度学习的框架不可避免地需要标记数据作为训练的基本事实。另一方面，单目里程计系统无法恢复绝对比例。必须引入外部或先验信息以进行规模恢复。为了解决这些问题，我们提出了一种新颖的基于深度学习的 RGB-D 视觉里程计系统。我们的两个主要贡献是：（i）在网络训练和姿态估计期间，将深度图像输入网络与 RGB 图像形成双流结构，并提出了双流深度神经网络。(ii) 系统采用无监督的端到端训练方法，因此不需要劳动密集型的数据标记任务。我们已经在 KITTI 数据集上测试了我们的系统，结果表明，所提出的 RGB-D 视觉里程计 (VO) 系统在平移和旋转误差方面比其他最先进的系统具有明显的优势。