%0 Journal Article %A ZHI Henghui %A YIN Chenyang %A LI Huibin %T Review of Visual Odometry Methods Based on Deep Learning %D 2022 %R 10.3778/j.issn.1002-8331.2203-0480 %J Computer Engineering and Applications %P 1-15 %V 58 %N 20 %X 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. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2203-0480