Affiliations: [a] College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, Shanxi, China
| [b] College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, China
| [c] Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan University of Technology, Taiyuan, Shanxi, China
| [d]
Intelligent Perception Engineering Technology Center of Shanxi, Taiyuan, China
Abstract: In the field of simultaneous localization and mapping (SLAM), visual odometry (VO) always has great application prospects. In recent years, with the progress in the field of machine learning, methods based on neural networks are constantly being updated and applied. In this paper, we propose a continuous and generalized monocular visual odometry method based on features and neural networks. First, the feature information of adjacent image sequences is extracted by matching and troubleshooting algorithm (FLANN_PSC-RANSAC), then it and the corresponding six-degree-of-freedom information are simultaneously input into the long short-term memory artificial neural network (LSTM) for model construction, which not only ensures the reliability of the mode but also eliminates the influence of illumination on the data. In the real environment test, it has been effectively proved in terms of trajectory recovery accuracy and generalization ability to different environments and different illuminations.
