## 7 Conclusions  
This study proposes a PE-SLAM algorithm based on ORB-SLAM2, which combines an improved particle swarm optimization algorithm with epipolar constraints to improve the accuracy and robustness of the motion model in a photovoltaic power station environment. Experimental results show that in unstructured scenes, ORB+PE-match has an average improvement in matching accuracy of 19.5%, 14.0%, and 6.0% compared to the traditional ORB algorithm, ORB+HAMMING algorithm, and ORB+RANSAC algorithm, with better recall rates. In trajectory experiments using the TUM dataset, PE-SLAM reduces the average absolute trajectory error and relative pose error by 29.1% and 27.0%, respectively, compared to the ORB-SLAM2 algorithm. Mapping experiment results demonstrate the successful application of PE-SLAM in a photovoltaic power station scenario, where the dense point cloud map accurately and completely reflects the scene, effectively overcoming challenges posed by unstructured factors. Therefore, the proposed PE-SLAM algorithm offers an effective solution to address the challenges that ORB-SLAM faces in a photovoltaic power station environment.  
PE-SLAM can be applied to cleaning or inspection robots in photovoltaic power station scenarios. Without external information support (such as GPS), it provides the robot with self-localization and 3D point cloud information about the environment using only the robot's own vision sensors. The mapping experiments in photovoltaic power  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset.**} & \multirow{2}{*}{**Scene**} & **Degree of Feature Similarity** & **Degree of Weak Texture** & \begin{tabular}{c} **Degree of Structural** \\ **Simplicity** \\ \end{tabular} & \begin{tabular}{c} **Degree of** \\ **Current Noise** \\ \end{tabular} & \begin{tabular}{c} **ATE (REMS)** \\ **of PE-SLAM** \\ \end{tabular} & \begin{tabular}{c} **ATE** \\ **of PE-SLAM** \\ \end{tabular} & \
\begin{tabular}{c} **PE-SLAM** \\ **Reduction** \\ **Rate** \\ \end{tabular} &
\begin{tabular}{c} **Mapping** \\ **Performance** \\ \end{tabular} \\ \hline \multirow{3}{*}{TUM} & structure\_notexture & High & High & Moderate & Low & 0.026 & 13.3\% & High \\  & nostructure\_texture & Moderate & Low & High & Low & 0.070 & 44.0\% & Moderate \\  & structure\_texture & Low & Low & Moderate & Low & 0.014 & 30.0\% & High \\ \cline{1-1} \cline{2-11} \cline{3-11} \cline{3-11} \cline{3-11} \cline{3-11} \cl  
station scenarios show that camera noise has a certain impact on the mapping results. For practical application of the algorithm, we believe that using RGB-D cameras with better anti-glare capabilities can improve the performance of PE-SLAM. From the parameter selection experiments, it can be seen that some parameters significantly affect PE-SLAM's performance. Therefore, optimal parameter selection is necessary for different scenarios. Future research could consider incorporating adaptive parameter algorithms to make PE-SLAM simpler and more effective in various unstructured environments. Additionally, PE-SLAM currently cannot modify the already-generated 3D point cloud based on back-end optimization results. If future research can address this, we believe the mapping performance will improve.  
Conceptualization, C.L. and Z.S.; methodology, C.L. and Z.S.; software, Z.S.; validation, C.L., Z.S., J.W. and K.Y.; formal analysis, Z.S. and K.Y.; investigation, W.N. and Z.S.; resources, W.N.; data curation, Z.S.; writing--original draft preparation, C.L. and Z.S.; writing--review and editing, C.L.; visualization, Z.S.; supervision, C.L.; project administration, C.L.; funding acquisition, C.L. and W.N. All authors have read and agreed to the published version of the manuscript.  
The authors declare no conflicts of interest.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.