## 5 Results
### SLAM Trajectory Experiment
#### 5.2.3 Experimental Results and Analysis  
Experimental verification of PE-SLAM and ORB-SLAM2 is conducted, with absolute trajectory errors shown in Table 2 and relative reduction rates shown in Table 3.  
It can be seen that the motion model improvement based on PE-match has brought about an enhancement in system accuracy. In the case of nostructure_texture, where only texture is present without contours, PE-SLAM achieves a 44.0% reduction in error, indicating that the improved algorithm provides better system accuracy in situations where textures are similar and lack contour structures. Table 4 shows the relative pose errors, while Table 5 presents the relative reduction rate of pose errors. It can be observed that the reduction effect of relative pose errors is consistent with that of absolute trajectory errors. In ORB-SLAM2, the strategy for backend optimization involves local trajectory optimization after tracking completion and before the arrival of the next frame. The tracking time needs to be minimized to ensure the effectiveness of backend trajectory optimization. The fact that the absolute trajectory errors are smaller than the relative pose errors in PE-SLAM indicates that the improved algorithm's tracking time meets the real-time requirements for system backend optimization. The average tracking time for each sequence is detailed in Table 6.  
As shown in Figures 14-16, based on the ground truth provided by the TUM dataset and the CameraTrajectory obtained from the SLAM algorithm, six trajectory error plots is \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Sequences** & **ORB-SLAM2** & **PE-SLAM** \\ \hline structure\_notexture & 10 & 35 \\ nostructure\_texture & 21 & 68 \\ structure\_texture & 25 & 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average tracking time (ms) of PE-SLAM and 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 structure\_notexture & 0.030 & 0.026 & 0.014 & 0.026 & 0.022 & 0.013 \\ nostructure\_texture & 0.125 & 0.101 & 0.074 & 0.070 & 0.063 & 0.030 \\ structure\_texture & 0.020 & 0.019 & 0.007 & 0.014 & 0.013 & 0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Absolute trajectory errors (ATE, m) of PE-SLAM and ORB-SLAM2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experimental Sequences** & **REMS** & **MEAN** & **S.D.** \\ \hline structure\_notexture & 13.3\% & 15.4\% & 7.1\% \\ nostructure\_texture & 44.0\% & 37.6\% & 59.5\% \\ structure\_texture & 30.0\% & 31.6\% & 28.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relative pose errors (RPEs, m) of PE-SLAM and 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 structure\_notexture & 0.046 & 0.039 & 0.024 & 0.043 & 0.036 & 0.023 \\ nostructure\_texture & 0.255 & 0.164 & 0.195 & 0.113 & 0.092 & 0.066 \\ structure\_texture & 0.032 & 0.028 & 0.015 & 0.026 & 0.022 & 0.012 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Relative reduction in relative pose Error (%) of PE-SLAM compared to ORB-SLAM2. generated. In the trajectory error plot, the actual trajectory is represented by a black line segment, while the estimated trajectory is depicted by a blue line segment. Red short lines indicate the distance difference between the predicted position and the actual position of the camera at the same time point. The length of the red short lines visually reflects the degree of error. As shown in Figures 17-19, we generate six dense point cloud maps based on the three image sequences mentioned above: \"structure_notextur  
Figure 16: Trajectory error plot of the structure_texture sequence.