## 3 Improved Motion Model Based on PE-Match  
The \"TRACKING\" module of ORB-SLAM2 can be divided into two main stages during the tracking process: pose estimation and pose optimization. The pose estimation stage employs three strategies: tracking based on reference keyframes, predictive tracking based on motion models, and relocalization. In the pose optimization stage, by establishing the matching relationship between map points of the current frame and its adjacent keyframes, more feature point matches are obtained to optimize and adjust the previous pose estimation. The primary task of tracking in the pose estimation stage is handled by the motion model. As shown in Figure 3, its basic idea is to assume the camera is in uniform motion over a short period, using the pose and velocity estimated from the previous frame to predict the approximate positions of matching feature points between the current frame and the previous frame. A search area is then set around this position for feature descriptor matching, which improves matching speed while ensuring good accuracy. The specific process is as follows:  
Step 1: Set the success criteria for feature matching: minimum distance < 0.9\(\times\) next smallest distance. Step 2: Initialize the bit position R, t of the current frame using the current motion velocity and the bit position of the previous frame. Step 3: Using the bit position R and t of the current frame, project the 3D point of the previous frame to the current coordinate system, search for feature points that can be matched within the radius t of that 2D point, traverse the points that can be matched, calculate the descriptor distance, and record the minimum matching distance. Figure 2: PE-SLAM system framework diagram. Step 4: Record the matching point feature directions for direction verification, eliminate the clusters of point pairs with a small number of the same directions in the direction histogram, and retain the clusters of the top three most numerous direction point pairs. Step 5: If fewer than 20 matching point pairs are found, expand the search radius t = 2 \(\times\) t and search again. Step 6: Perform G2O map optimization of the bit pose of the current frame using matched point pairs. Step 7: Based on the number of successful matches, determine whether the tracking is successful or not. The motion model in ORB-SLAM2 only utilizes orientation histograms to remove outliers from feature matching results, which leads to fast processing but mediocre removal of incorrect matches. Addressing this issue, this paper proposes a method that, after filtering with orientation histograms in Step 4, employs a particle swarm combined with epipolar constraints to further eliminate incorrect matches. This secondary filtering step can remove the majority of incorrect matches, thereby enhancing the accuracy of the SLAM system. 4 Feature Matching Algorithm Based on Particle Swarm Optimization and Epipolar Constraint (PE-Match)  
The traditional ORB feature matching algorithm has the advantage of fast computation speed. However, in photovoltaic power plant images, there are non-structural factors such as high feature similarity and simple structures, leading to a high occurrence of incorrect matches with the ORB algorithm. As mentioned earlier, feature matching in photovoltaic power plant scenes exhibits global feature similarity, with incorrect matches lacking consistency and not possessing the same constraint properties, while correct matches generally exhibit the same constraint properties. Therefore, PE-match is designed to address the characteristics of incorrect matches in photovoltaic power plant scenes. It utilizes the particle swarm algorithm's information exchange and parallel search capabilities to search for constraints that match the majority of point pairs in the coarse matching point set and ultimately eliminates outliers based on these constraints. The epipolar constraint leverages rotation and translation information in images and is minimally affected by large disparities between images.