A review of monocular visual odometry

• Published in: The Visual computer Vol. 36; no. 5; pp. 1053 - 1065
• Main Authors: He, Ming, Zhu, Chaozheng, Huang, Qian, Ren, Baosen, Liu, Jintao
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2020; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Binocular vision; Cameras; Computer Graphics; Computer Science; Global optimization; Global positioning systems; GPS; Image Processing and Computer Vision; Localization; Obstacle avoidance; Optimization; Real time; Real time operation; Robots; Robustness (mathematics); Sensors; Simultaneous localization and mapping; Survey; Visual SLAM; Multi-sensor data fusion; Machine learning; Visual odometry
• ISSN: 0178-2789; 1432-2315
• DOI: 10.1007/s00371-019-01714-6

Monocular visual odometry provides more robust functions on navigation and obstacle avoidance for mobile robots than other visual odometries, such as binocular visual odometry, RGB-D visual odometry and basic odometry. This paper describes the problem of visual odometry and also determines the relationships between visual odometry and visual simultaneous localization and mapping (SLAM). The basic principle of visual odometry is expressed in the form of mathematics, specifically by incrementally solving the pose changes of two series of frames and further improving the odometry through global optimization. After analyzing the three main ways of implementing visual odometry, the state-of-the-art monocular visual odometries, including ORB-SLAM2, DSO and SVO, are also analyzed and compared in detail. The issues of robustness and real-time operations, which are generally of interest in the current visual odometry research, are discussed from the future development of the directions and trends. Furthermore, we present a novel framework for the implementation of next-generation visual odometry based on additional high-dimensional features, which have not been implemented in the relevant applications.