## 5 Results
### Simulation Experiments of Photovoltaic Scenarios
#### 5.3.2 Simulation Experiment Results and Analysis  
The evaluation metrics for the simulation trajectory experiment are the same as those for the dataset experiment. By conducting simulation experiments on the aforementioned Si, PE-SLAM and ORB-SLAM2 were validated. The absolute trajectory error validation values are shown in Table 7, and the relative reduction rates are shown in Table 8.  
As shown in Table 8, the trajectory errors of both ORB-SLAM2 and PE-SLAM in the Gazebo photovoltaic power station simulation scene are at the centimeter level, indicating that both algorithms achieve good localization accuracy in this scenario. Additionally,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Experimental Sequences**} & \multicolumn{3}{c}{**ORB-SLAM2**} & \multicolumn{3}{c}{**PE-SLAM**} \\ \cline{2-7}  & **REMS** & **MEAN** & **S.D.** & **REMS** & **MEAN** & **S.D.** \\ \hline Si & 0.026 & 0.024 & 0.009 & 0.023 & 0.021 & 0.009 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Absolute Trajectory Errors (ATE, m) of PE-SLAM and ORB-SLAM2.  
Figure 21: Photovoltaic power station simulation scene based on Gazebo.  
Figure 20: Robot model based on Gazebo.  
PE-SLAM demonstrates better system accuracy, reducing the absolute trajectory error by 11.5% compared to ORB-SLAM2.  
Tables 9 and 10 present the results of the relative pose error in the photovoltaic scene simulation. The fact that the relative trajectory error is larger than the absolute trajectory error indicates that the back-end optimization of both algorithms was not affected by the tracking thread's processing time, as both performed local and global trajectory optimization. The average tracking time for each sequence is shown in Table 11.  
As shown in Figure 22, the trajectory error plot s is drawn based on the ground truth of the robot's actual trajectory provided by the Gazebo simulation system and the camera trajectory (CameraTrajectory) derived from the algorithm. In the trajectory error graph, the ground truth trajectory is represented by black line segments, while the estimated trajectory is shown with blue line segments. Red short lines indicate the distance difference between the predicted and actual positions of the camera at the same time point, and the length of these red short lines visually reflects the degree of error.  
As shown in Figure 23, two dense point cloud maps is generated from the Si image sequence mentioned earlier. ORB-SLAM2 represents the dense point cloud map created by adding a dense mapping thread to the original algorithm, while PE-SLAM represents the dense point cloud map built using the poses obtained from the improved tracking thread. The mapping results for each sequence are shown in the following figures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Sequences** & **REMS** & **MEAN** & **S.D.** \\ \hline Si & 5.2\% & 3.0\% & 5.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Relative reduction in relative pose error (%) of PE-SLAM compared to ORB-SLAM2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Experimental Sequences**} & \multicolumn{3}{c}{**ORB-SLAM2**} & \multicolumn{3}{c}{**PE-SLAM**} \\ \cline{2-6}  & **REMS** & **MEAN** & **S.D.** & **REMS** & **MEAN** & **S.D.** \\ \hline Si & 0.095 & 0.066 & 0.067 & 0.090 & 0.064 & 0.063 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Relative pose errors (RPEs, m) of PE-SLAM and ORB-SLAM2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Sequences** & **ORB-SLAM2** & **PE-SLAM** \\ \hline Si & 23 & 49 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Average tracking time (ms) of PE-SLAM and ORB-SLAM2.  
Figure 22: Trajectory error plot of the Si.  
Due to the relatively uniform texture and structure of the photovoltaic power station simulation scene, which enhances the scene's unstructured factors, the mapping performance would be better in a real photovoltaic scene.