# A Lightweight Visual Odometry Based on LK Optical Flow Tracking
## 4 Conclusions  
In this paper, a lightweight, front-end VO based on optical flow tracking has been proposed to solve the problem that the visual SLAM system based on feature points has good robustness but poor real-time performance. Without calculating the descriptors, the reliable matching relationship of key points between the adjacent images has been established by combining the uniform motion model with the pyramid-based sparse optical flow method. GMS and RANSAC algorithms were used to filter out the outliers in turn. To verify the effectiveness of the proposed algorithm in different environments, experiments have been carried out in two datasets of TUM and ICL-NUIM and compared with ORB-SLAM3. According to the experimental results, although the RMSE of the proposed method is not much different from ORB-SLAM3, the proposed method can effectively improve the efficiency up to 27.3% on average, which will greatly reduce the computation quantity of the system.  
Figure 13: The trajectory position comparison diagram of the TUM dataset and the ICL-NUIM dataset. (**a**) fr1-room sequence; (**b**) fr3-office sequence; (**c**) fr2-person sequence; (**d**) fr2-large sequence; (**e**) living3 sequence.  
In the future, we will improve and refine our work further in the following aspects: First, according to our experimental results, using the same motion prediction model for different scenarios may lead to accuracy degradation. Thus, we will consider experimenting with different motion prediction models in the same scene to research the adaptability of the motion model to the scene. Second, in the experimental part of this paper, only comparative tests with ORB-SLAM3 have been performed at present; we will look for other representative methods to compare and contrast to increase the validity of the paper. Finally, the work presented in this paper does not involve the lower-layer control and estimation of robots yet. Thus, the adaptability of the improved algorithm to various types of lower-layer control is also a very valuable research direction.  
Conceptualization, X.W. and Y.C.; methodology, X.W. and Y.Z.; software, G.Y. and Y.Z.; validation; X.W. and Y.C.; formal analysis, G.Y., Y.Z. and Y.C.; investigation, Y.Z., G.Y. and Y.C.; resources, X.W. and G.Y.; data curation, G.Y., Y.Z. and Y.C.; writing--original draft preparation G.Y., writing--review and editing, Y.C. and Y.Z.; visualization, G.Y., Y.Z. and Y.C.; supervision, X.W. and Y.C.; project administration, X.W. and Y.C.; funding acquisition, X.W. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (51105213); the Demonstration and Guidance Special Project of Science and Technology benefit people in Qingdao (22-3-7-sm jk-11-nsh).  
The study was conducted by the Declaration of Helsinki, and approved by the Ethics Committee at the College of Electromechanical Engineering, Qingdao University of Science and Technology.  
Not applicable.  
Author [PERSON] was employed by the company Qingdao Anjie Medical Technology Co., Ltd., Qingdao 266000, China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.