With the continuous development of mobile robot technology, odometry technology has become a key technology for mobile robots to realize environmental perception, and its development level is of great significance to improving the autonomy and intelligence of robots. Firstly, the current development status of laser simultaneous localization and mapping (SLAM) and visual SLAM in simultaneous localization and mapping was systematically explained. The classic SLAM framework and its mathematical description were expounded, and the camera models of three common types of cameras and their mathematical description of visual odometry were briefly introduced. Secondly, the research progress of traditional visual odometry and deep learning odometry were systematically elaborated. The advantages and disadvantages of various mileage calculation methods in the past ten years were compared and analyzed. In addition, the performance of seven commonly used data sets was comparatively analyzed. Finally, the problems faced by odometry technology were summarized from the aspects of accuracy, robustness, data sets, and multi-modality, and five development trends of visual odometry were prospected from the aspects of improving the real-time performance and robustness of the algorithm. For the development of more intelligent and miniaturized new sensors, the integration with unsupervised learning, the improvement of semantic expression technology and the development of cluster robot collaboration technology were introduced.