Visual Odometry (VO),which is an important part of visual simultaneous localization and mapping technology,is mainly used for robot pose estimation and local map building through time series images captured by camera sensors.Known as the front end,VO has been widely used in many fields and achieved fruitful practical results,and it is of great significance for unmanned driving,autonomous drones,virtual reality,and augmented reality,etc.In this paper,we summarize the recent research results on the novel visual odometry technology based on introduction of various algorithms in the framework module of classical VO.According to their technical means,the novel methods are divided into two categories,including VO integrated with multiple sensors (take VIO as an example),and VO based on deep learning.The former improves the accuracy of VO by complementing the advantages of various sensors,while the latter is combined with deep learning network.Finally,the existing algorithms of visual odometry are compared,and the future development trend is forecasted based on the challenges faced by VO.