# PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment
## 5 Results
### Accuracy Evaluation of Pose Trajectories
Figure 8 shows the trajectory comparison of the three algorithms in the industrial factory scene of sequence MH_01_easy and the indoor room scene of sequence V1_03_difficult. Compared with PL-VINS, the trajectory accuracy in all scenarios of EuRoC datasets in this paper has smaller errors, especially in difficult scenarios. In all easy scenarios, the RMSE of the proposed PLI-VINS is 0.083, PL-VINS is 0.107, and VINS-Mono is 0.155, respectively, and the trajectory accuracy is improved by 46.5% and 30.9%. However, in difficult scenarios, the trajectory accuracy of the proposed PLI-VINS is improved by 41.3%, while PL-VINS is only 21.1%. It \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**w/o Loop**} & & & & \\ \cline{2-9}
**Seq** & **VINS-Mono** & **PL-VINS** & **PL-VIO** & **Ours** & **Ours** & **(Length** & **VINS-Mono** & **PL-VINS** & **Ours** \\  & & & & & **Filter**) & **Line Large** & & & \\ \hline MH\_01\_easy & 0.244 & 0.172 & 0.152 & 0.232 & **0.146** & 0.183 & 0.137 & **0.108** \\ MH\_02\_easy & 0.222 & 0.193 & 0.173 & 0.163 & **0.142** & 0.175 & 0.159 & **0.090** \\ MH\_03\_medium & 0.307 & 0.255 & 0.265 & 0.222 & **0.214** & 0.265 & 0.136 & **0.097** \\ MH\_04\_difficult & 0.373 & 0.299 & 0.363 & **0.216** & **0.216** & 0.305 & 0.240 & **0.174** \\ MH\_05\_difficult & 0.421 & 0.384 & 0.277 & 0.255 & **0.253** & 0.346 & 0.369 & **0.222** \\ V1\_01\_easy & 0.161 & **0.069** & 0.098 & 0.075 & 0.074 & 0.145 & **0.056** & 0.067 \\ V1\_02\_medium & 0.110 & 0.140 & & **0.123** & **0.123** & 0.108 & 0.097 & **0.088** \\ V1\_03\_difficult & 0.325 & 0.180 & 0.201 & 0.183 & **0.148** & 0.219 & 0.162 & **0.141** \\ V2\_01\_easy & 0.135 & 0.097 & 0.092 & 0.103 & **0.077** & 0.115 & 0.078 & **0.068** \\ V2\_02\_medium & 0.328 & **0.112** & 0.155 & 0.147 & 0.133 & 0.259 & **0.089** & 0.119 \\ V2\_03\_difficult & 0.370 & 0.214 & 0.294 & **0.136** & 0.188 & 0.303 & 0.160 & **0.152** \\ Mean & 0.272 & 0.192 & 0.210 & 0.169 & **0.156** & 0.220 & 0.153 & **0.120** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMSE ATE (M) comparison of VINS-Mono, PL-VINS, PL-VIO and ours (PL-VINS). is not difficult to find by referring to the trajectory comparison diagram in Figure 8a,b that in difficult type scenes, the trajectory accuracy of the PLI-VINS is improved more. Figure 8: Comparison of trajectories of VINS-Mono, PL-VINS, and PLI-VINS in industrial factories and indoor rooms. The gray dashed lines are the trajectory truth value, and the blue, green and red solid lines are PL-VINS, the proposed PLI-VINS and the VINS-Mono: (**a**) sequence MH_01_easy scene respectively; (**b**) sequence V1_03_difficult scenario. Figure 7: Pose error trajectory comparison of VINS-Mono and ours W/and W/O loop algorithms under sequence MH_03_medium, V1_01_easy and V2_01_easy. The color of the track changes from blue to red, and the closer the color is to red, the greater the error. The gray dotted line is the trajectory truth: (**a**) VINS-Mono w/o loop; (**b**) VINS-Mono; (**c**) ours (length gilter) w/o loop; (**d**) ours (length filter and line merge) w/o loop; (**e**) ours.