In the last decade, supervised deep learning approaches have been extensively employed in visual odometry (VO) applications, which is not feasible in environments where labelled data is not abundant. On the other hand, unsupervised deep learning approaches for localization and mapping in unknown environments from unlabelled data have received comparatively less attention in VO research. In this study, we propose a generative unsupervised learning framework that predicts 6-DoF pose camera motion and monocular depth map of the scene from unlabelled RGB image sequences, using deep convolutional Generative Adversarial Networks (GANs). We create a supervisory signal by warping view sequences and assigning the re-projection minimization to the objective loss function that is adopted in multi-view pose estimation and single-view depth generation network. Detailed quantitative and qualitative evaluations of the proposed framework on the KITTI and Cityscapes datasets show that the proposed method outperforms both existing traditional and unsupervised deep VO methods providing better results for both pose estimation and depth recovery.

本文提出了一种基于生成对抗网络的无监督学习框架，从未标记的RGB图像序列中预测场景的6自由度姿态相机运动和单目深度图，并在KITTI和Cityscapes数据集上给出了详细的定量和定性评估，结果表明该方法优于现有传统和无监督深度VO方法，提供更好的姿态估计和深度恢复结果。

GANVO: 基于生成对抗网络的无监督单目视觉里程计与深度估计