## 6 Discussion  
In the photovoltaic power station scenario, the feature similarity makes the ORB algorithm prone to mismatches during the feature matching stage. Extensive experiments have shown that these mismatches are random and lack consistent constraints, while correct matches share the same constraint conditions. Based on the characteristics of mismatches generated by ORB in the photovoltaic station scenario, an improved particle swarm optimization (PSO) algorithm is used to identify the constraint conditions that comply with the majority of matched point pairs. This helps in determining the correct matching constraints and subsequently eliminating mismatches. The epipolar constraint, which includes both intrinsic and extrinsic camera parameters, is suitable for both near and far points and has an analytical solution that meets the real-time requirements of SLAM algorithms. Therefore, this paper chooses the epipolar constraint as the constraint condition. Since the PSO algorithm is prone to local optima, a new mutation strategy is designed  
Figure 27: The 3D map generated from Real sequence.  
Figure 26: The 3D map generated from In sequence.  
to perturb the Pbest of the particle swarm, enhancing the global search capability of the algorithm in the later stages. This improves the PSO algorithm's ability to address the sensitivity of epipolar constraints to mismatches. Based on the above theory, the paper designs PE-match to improve the motion model of ORB-SLAM2, thereby reducing the number of mismatches in photovoltaic power station scenarios and improving SLAM's localization accuracy and mapping performance.  
Table 12 presents the characteristics and main experimental results of PE-SLAM in the six experimental scenarios mentioned earlier. The TUM dataset is more authoritative, and based on the experimental results, PE-SLAM has achieved better localization accuracy and mapping performance compared to ORB-SLAM2. In the structure_notexture and structure_texture scenarios, the ATE (REMS) is at the centimeter level, while in the nostructure_texture scenario, the ATE (REMS) is at the decimeter level, with the most significant improvement in this scenario, achieving an ATE (REMS) reduction rate of 44.0%. In the simulation experiments, the ATE (REMS) is only 0.023, indicating that PE-SLAM is suitable for the photovoltaic power station simulation scenarios in terms of localization accuracy. However, the mapping performance is moderate, which we believe is due to the high degree of texture similarity in the simulation scenarios. In real photovoltaic power station scenarios, the presence of background buildings, ground, and stained photovoltaic panels weakens the unstructured scene factors. Theoretically, this should result in better mapping performance, which is confirmed by the mapping experiments in the In scenario. The mapping performance in the Real scenario is noticeably lower than in the In scenario, which is due to the significant influence of outdoor lighting on the RGB-D camera in outdoor environments. Overall, PE-SLAM has surpassed ORB-SLAM2 in both localization accuracy and mapping performance in the unstructured environments of photovoltaic power stations.