Self-Supervised Learning of Visual Odometry

Abstract

Monocular visual odometry (VO) based on deep learning methods is one of the most important research tasks in computer vision in recent decades. Unlike unsupervised and supervised methods, Self-supervised learning can add additional image information to improve the accuracy and robustness of the model without real labels, so the self-supervised visual odometry methods have received widespread attention. However, most self-supervised VO methods do not make full use of self-supervised signals, especially optical flow as a self-supervised signal. In this paper, we make full use of optical flow as a self-supervised signal, a new self-supervised network structure is proposed. This self-supervised network structure adds optical flow as a self-supervised signal to the basic cascaded VO estimation network. While providing additional information to train the camera pose estimation, the optical flow is used to generate mask to solve the occlusion problems that often occur in self-supervised models. We evaluated the self-supervised visual odometry model proposed in this paper on the KITTI dataset. Experiments show that our method has achieved good performance in depth estimation and camera pose estimation.