## 5 Experimental Validation
### EuroC MAV Dataset  
The EuRoC Micro Aerial Vehicle (MAV) dataset was collected by a quadcopter in three different environments, including the machine hall and the VICON room. In experiments of this paper, only the left camera image frames were used for pose estimation. Figure 5 shows the experimental snapshots of the proposed method under 11 sequences. From the figure, it can be seen that the proposed method still has good robustness in low-illumination environments. The root mean square error (RMSE) of the absolute trajectory error was used to evaluate localization accuracy, while the average RMSE of each VIO system was calculated to demonstrate performance improvement. The RMSE values of VINS-Mono, PL-VINS, UV-SLAM, and the proposed method for 11 sequences are shown in Table 4, with the values denoted in bold black being closest to the true value. Localization errors were evaluated by the root mean square error from the Evo package ([[https://github.com/[PERSON]/evo](https://github.com/[PERSON]/evo)]([https://github.com/[PERSON]/evo](https://github.com/[PERSON]/evo))) and two-dimensional pose trajectories were evaluated by the MATLAB 2018a_win64 software package. The MH scene is narrower and full of texture, and the theoretical case works better under the point feature system VINS-Mono, but according to Table 4, the proposed method shows superiority in all five sequences. The VICON scene motion blur is prone to degradation, and the proposed algorithm introduces structural and non-structural line constraints to reduce the mapping error compared with the PL-VINS. Figure 6 provides an evolution of localization results of the proposed method in three directions _xyz_ under V1_02_medium. As can be seen from the figure, the local \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **VINS-Mono** & **PL-VINS** & **UV-SLAM** & **Proposed** \\ \hline MH\_01\_easy & 0.230 & 0.234 & 0.212 & 0.200 \\ MH\_02\_easy & 0.164 & 0.160 & 0.154 & 0.148 \\ MH\_03\_medium & 0.251 & 0.250 & 0.247 & 0.238 \\ MH\_04\_difficult & 0.462 & 0.358 & 0.356 & 0.351 \\ MH\_05\_difficult & 0.334 & 0.326 & 0.325 & 0.313 \\ V1\_01\_easy & 0.085 & 0.083 & 0.078 & 0.078 \\ V1\_02\_medium & 0.142 & 0.165 & 0.106 & 0.096 \\ V1\_03\_difficult & 0.184 & 0.178 & 0.174 & 0.167 \\ V2\_01\_easy & 0.097 & 0.098 & 0.091 & 0.086 \\ V2\_02\_medium & 0.266 & 0.111 & 0.114 & 0.112 \\ V2\_03\_difficult & 0.328 & 0.351 & 0.334 & 0.333 \\ Average & 0.231 & 0.201 & 0.199 & 0.193 \\ Enhance & 19.07\% & 8.35\% & 2.88\% & — \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSE APE (meter) of VINS-Mono, PL-VINS, UV-SLAM, and the proposed method. Figure 5: The experiment snapshots of the EuRoC dataset by the proposed method. ization results improve significantly with time. Figure 7 shows the trajectory maps of MH_02_easy and V1_02_medium sequences in the plane. It can be seen that in most scenarios, the proposed method obtains the trajectory estimation closest to the true value in the comparison experiments. Figure 6: Evolution of localization with respect to the V1_02_medium sequence (blue solid lines: the proposed method; blue dashed lines: PL\(-\)VINS; green solid lines: UV\(-\)SLAM; red solid lines: VINS\(-\)Mono; and red thick dashed lines: ground truth). Figure 7: Comparative paths with respect to V1_02_medium (**left**) and MH_02_easy (**right**) (blue solid lines: the proposed method; blue dashed lines: PL\(-\)VINS; green solid lines: UV\(-\)SLAM; red solid lines: VINS\(-\)Mono; and red thick dashed lines: ground truth). Figure 8 demonstrates the comparison plot of pose trajectory error between UV-SLAM and the proposed method on MH_03_medium, V1_03_ difficult, and V2_01_easy sequences, where the redder color of the trajectory represents the larger error. From the above results, it is clear that the proposed method has a smaller error compared with other methods.