## 1 Introduction
Reference [17] used the RANSAC algorithm to remove mismatches in coarse matching point sets, achieving high real-time performance and effectively enhancing feature matching accuracy. Reference [18] utilized the PROSAC algorithm to remove mismatches by sampling from an ever-expanding best set, reducing computational load compared to Reference [17], but the randomness of sampling decreased the algorithm's robustness. Reference [19] employed the particle swarm algorithm with feature point similarity as the fitness function for feature point matching, providing effective matching accuracy and robustness, albeit with poorer real-time performance. Reference [20] addressed mismatch removal in remote sensing images using the particle swarm algorithm combined with affine transformations, showing good real-time performance and matching accuracy but limited applicability across various scenes. Reference [21] introduces a hardware accelerator for ORB-SLAM that optimizes rBRIEF descriptor generation using a genetic algorithm, reducing energy consumption by 14,597\(\times\) and 9609\(\times\) on CPU and GPU, respectively. Reference [22] enhances ORB feature extraction in complex lighting by combining image enhancement with a truncated adaptive threshold, improving feature point detection and robustness. Reference [23] introduces AAM-ORB, which integrates an affine attention module into the ORB feature matching pipeline to improve feature matching accuracy and efficiency under scene changes, using grid-based motion statistics to enhance speed. Reference [24] introduces a vision-based spatial target recognition method for robotic arms, combining improved ORB feature extraction with an enhanced GMS-MLESAC algorithm to achieve precise, fast, and stable recognition of spatial targets for complex task execution. Reference [25] improves ORB feature matching by introducing a signature-based method that significantly reduces the number of features needing comparison, thereby speeding up the matching process while maintaining high precision. The methods mentioned above enhance the accuracy or speed of the ORB algorithm. However, due to the challenges posed by unstructured factors in the photovoltaic power plant environment for feature matching and the real-time requirements of visual SLAM systems, some of these proposed improvements are not suitable for visual SLAM algorithms in unstructured environments. This paper proposes a method for removing ORB image feature mismatches based on an improved particle swarm algorithm combined with epipolar constraints (PE-match), focusing on finding the majority of correct matches with consistent constraint conditions among the coarse matching point pairs. Compared to matching methods based on random sampling and clustering, the particle swarm algorithm has better global search capability and, compared to genetic algorithms, has lower algorithm complexity. Epipolar constraints offer better robustness against changes in perspective compared to affine transformations, and the analytical solution provided by epipolar constraints ensures the real-time performance of the algorithm. PE-match addresses the sensitivity of epipolar constraints to mismatches by leveraging the optimization capability of the improved Particle Swarm Optimization Algorithm, effectively expanding the application environment of epipolar constraints. PE-SLAM employs PE-match to improve the motion model of ORB-SLAM2, enhancing the tracking accuracy of the motion model in unstructured environments of photovoltaic power plants. The improved motion model, as the main tracking module within ORB-SLAM2, further increases the localization and mapping accuracy of ORB-SLAM2 in photovoltaic power plant scenes.