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.

中文翻译：

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单目视觉里程计综述

与双目视觉里程计、RGB-D 视觉里程计和基本里程计等其他视觉里程计相比，单目视觉里程计为移动机器人提供了更强大的导航和避障功能。本文描述了视觉里程计的问题，并确定了视觉里程计与视觉同步定位和映射（SLAM）之间的关系。视觉里程计的基本原理是用数学的形式表达的，具体是增量求解两个系列帧的位姿变化，通过全局优化进一步改进里程计。在分析了实现视觉里程计的三种主要方式后，还详细分析和比较了最先进的单目视觉里程计，包括 ORB-SLAM2、DSO 和 SVO。鲁棒性和实时操作的问题，哪些是目前视觉里程计研究普遍感兴趣的，从未来的发展方向和趋势来讨论。此外，我们提出了一个新的框架，用于基于额外的高维特征实现下一代视觉里程计，这些特征尚未在相关应用程序中实现。