## 1 Introduction  
Simultaneous Localization and Mapping (SLAM) is an essential task in robotics and computer vision that aims to simultaneously estimate a robot's pose and construct a map of the environment [1]. As images and videos can provide detailed information about the environment, a significant portion of SLAM research focuses on visual SLAM (VSLAM) [2]. Feature point tracking is a critical element in VSLAM since it has a significant impact on the accuracy of localization and mapping in later stages. The classic algorithms for feature point tracking include the Lucas-Kanade algorithm [3, 4], BRIEF algorithm [5], SIFT algorithm [6], SURF algorithm [7] and ORB algorithm [8, 9]. The classic algorithms for feature point tracking can be divided into the feature point descriptor method [10, 11, 12, 13] and optical flow method [14, 15, 16, 17]. The feature point descriptor method involves extracting feature points from two image frames, computing their descriptors, and matching the feature points based on the similarity of their descriptors to achieve feature point matching, such as SIFT [6], SURF [7], ORB [8], and BRIEF [5]. Furthermore, the optical flow method determines the direction and speed of each pixel's movement on the image plane by calculating the change in pixel gray value between adjacent frames, such as dense optical flow [18, 19, 20] and sparse optical flow [21, 22, 23]. In the process of feature point tracking, to improve visual SLAM positioning accuracy, it is necessary to eliminate mismatched point pairs caused by environmental light changes, motion blur, noise, and other factors. For the feature point descriptor, brute force matching is a common method used [24]. In this method, each feature point in one image is compared with every feature point in the other image to find the best match. This is achieved by calculating a distance measure between the feature descriptors. The feature point with the smallest distance is considered to be the best match. Although brute force matching is very simple, the initial brute force matching contains many false matching results. This is because brute force matching only considers that the two descriptors are the most similar to other descriptors, and does not consider that the feature points in image \(A\) are not the same point in image \(B\). Thus, brute force matching will match each feature point in image \(A\) with a feature point in image \(B\), regardless of whether the matching point exists. Therefore, some researchers use the global distance threshold to reduce the result of mismatching. Only when the distance between two feature point descriptors is less than the set threshold are the two feature points considered matching point pairs. Nonetheless, there are notable variations in the distinguishability of feature point descriptors across different scenarios, necessitating ongoing debugging and threshold determination. Furthermore, dealing with feature points that have inconsistent distinguishability is also a challenging task [25]. Cross-validation is a common method in brute force matching [26]. For the feature point \(p_{i}\) in image \(A\), the most similar feature point in image \(B\) is \(q_{i}\), and for \(q_{i}\), the most similar feature point in image \(A\) is also \(p_{i}\), that is, the two feature points are the most similar to each other, so it is considered that \((p_{i},q_{i})\) is a pair of matching points. Although cross-validation can reduce the mismatch of brute force matching to a certain extent, the effect is limited. RANSAC [27] can effectively remove mismatching points; however, RANSAC also removes some correct matching points, reducing the number of matching point pairs [28], which has a negative impact on the subsequent position and attitude estimation due to these erroneous judgements. For the optical flow method, reverse optical flow is a common method [29; 30]. The idea of reverse optical flow is very simple.