# A Lightweight Visual Odometry Based on LK Optical Flow Tracking
## 1 Introduction  
With the rapid growth of computer science, sensors, and other advanced technology industries, the application of mobile robots has surged and become a hot research topic in recent years. SLAM technology solves the problems of mobile robots' positioning and the construction of surrounding environment maps [1; 2]. Currently, SLAM technology mainly consists of visual SLAM [3], laser SLAM [4], and multi-sensor fusion SLAM [5; 6; 7; 8; 9; 10]. Laser SLAM is relatively mature and stable, but multi-line laser radar is expensive [11], and its repositioning ability is poor. Visual SLAM has the advantages of simple structure, low cost, small size, and the ability to obtain abundant environmental information. Proper fusion of multi-sensor measurements can improve SLAM performance, but the relative positional transformations between different sensors need to be calibrated before fusion, and they are susceptible to failure due to degrading motion. As visual SLAM technology continues to become mature, mobile robots are increasingly used in various fields of application. Visual Odometry (VO) is the front end of visual SLAM, which uses the image information provided by visual sensors to complete the preliminary estimation of camera position between adjacent images and construct a local map. VO is the core part of visual SLAM, which can be divided into feature point methods [12] and direct methods [13] based on different feature association methods. Feature point methods are represented by Mono-SLAM [14], PTAM [15], and ORB-SLAM [16; 17; 18; 19], which rely on multi-view geometry extract the key points from the camera input images and calculate descriptors first, and then the data association between adjacent images is established via descriptor matching, and finally the camera position is solved by minimizing the reprojection error between thecorresponding key points. Unlike feature point methods, direct methods represented by DTAM [20], LSD-SLAM [21], SVO [22], DSO [23], and VINS-mono [24] are not needed to calculate the key points and descriptors in the image; by minimizing the photometric error between pixels, the initial camera position is solved at the same time. Feature points methods have excellent robustness and accuracy, but it is necessary to calculate the key points and descriptors for all images to establish the feature-matching relationship, and the calculation and matching of descriptors are time-consuming, resulting in the poor real-time performance of the system [25]. Direct methods solve the position of the camera by minimizing the photometric error of the pixels. Since there is no need to calculate the key points and descriptors, the method has good real-time performance, excellent results can be achieved in low-texture environments, and it is easier to build dense maps of indoor scenes. However, the assumption of grayscale invariance is too idealistic and difficult to realize in practical applications. Moreover, the image is non-convex, so it is necessary to provide a reliable initial value for the optimization algorithm to avoid its non-convergence [26; 27]. [PERSON] et al. [28] combine the standard KLT method with an epipolar constraint to substitute feature-based stereo matching, which achieved a better trade-off between efficiency and accuracy. Duo-VIO [29] applies a small baseline stereo camera with IMU, integrated feature observations, and IMU measurements obtained from the camera images to estimate the orientation, location, and velocity of the camera, it allows the system to execute pose estimation more rapidly and accurately. To deal with the problem of good robustness but poor real-time performance in visual SLAM systems based on feature points, a lightweight VO based on optical flow tracking by combining feature point methods based on the ORB-SLAM3 in visual mode with sparse optical flow methods is proposed in this paper.