Abstract: Visual odometry（VO） is a common method to deal with the positioning of mobile devices equipped with vision sensors, and has been widely used in autonomous driving, mobile robots, AR/VR and other fields. Compared with traditional model-based methods, deep learning-based methods can learn efficient and robust feature representations from data without explicit computation, thereby improving their ability to handle challenging scenes such as illumination changes and less textures. In this paper, it first briefly reviews the model-based visual odometry methods, and then focuses on six aspects of deep learning-based visual odometry methods, including supervised learning methods, unsupervised learning methods, model-learning fusion methods, common datasets, evaluation metrics, and comparison of models and deep learning methods. Finally, existing problems and future development trends of deep learning-based visual odometry are discussed.

Key words: visual odometry, deep learning, pose estimation, V-SLAM

摘要： 视觉里程计（visual odometry，VO）是处理搭载视觉传感器的移动设备定位问题的一种常用方法，在自动驾驶、移动机器人、AR/VR等领域得到了广泛应用。与传统基于模型的方法相比，基于深度学习的方法可在不需显式计算的情况下从数据中学习高效且鲁棒的特征表达，从而提升其对于光照变化、少纹理等挑战性场景的鲁棒性。简略回顾了基于模型的视觉里程计方法，从监督学习方法、无监督学习方法、模型与学习融合方法、常用数据集、评价指标、模型法与深度学习方法对比分析六个方面全面介绍了基于深度学习的视觉里程计方法。指出了基于深度学习视觉里程计仍存在的问题和未来的发展趋势。

关键词: 视觉里程计, 深度学习, 位姿估计, V-SLAM