# PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment
## 5 Results
### Accuracy Evaluation of Pose Trajectories  
In this subsection, the positioning accuracy analysis is performed on all sequences in the EuRoC datasets, and the PLI-VINS is compared with VINS-Mono, PL-VINS and PL-VIO, respectively. The absolute trajectory errors of different algorithms under the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Seq**} & \multirow{2}{*}{**LSD + LBD + KNN**} & \multicolumn{3}{c}{**LSD (Fixed Threshold) +**} & \multicolumn{3}{c}{**Ours + LBD + KNN**} \\ \cline{3-8}  & & & **LBD + KNN** & **Length Filter** & \multicolumn{3}{c}{**Length Filter and**} \\ \hline  & **Num** & **Time (s)** & **Num** & **Time (s)** & **Num** & **Num** & **Time (s)** \\ \hline MH\_01\_easy & 2480 & 5.93 & 838 & 3.78 & 234 & 179 & 3.4 \\ MH\_03\_medium & 2328 & 6.12 & 1135 & 3.58 & 157 & 132 & 2.13 \\ V1\_01\_easy & 1562 & 9.87 & 763 & 2.07 & 128 & 109 & 3.48 \\ V1\_03\_difficult & 1757 & 4.17 & 957 & 2.33 & 78 & 45 & 0.74 \\ V2\_01\_easy & 1249 & 3.3 & 868 & 3.3 & 117 & 96 & 1.44 \\ Mean & 1875 & 5.88 & 912.2 & 3.01 & 142.8 & 112 & 2.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average line feature extraction quantity and time for different algorithms. Figure 6: Graph of experimental results: (**a**) three scenarios of EuRoC datasets; (**b**) traditional LSD algorithm; (**c**) LSD algorithm with fixed threshold (line feature extraction algorithm in PL-VINS); (**d**) ours (length filtering); (**e**) ours (length filtering and line segment merging). EuRoC datasets are shown in Table 2, where the values with the lowest errors are in bold. In Figure 7, this paper shows the accuracy heatmap of VINS-Mono and our algorithm in the sequence MH_03_medium, V1_01_easy, V2_01_easy; the gray dotted line represents the true value of the trajectory, and the colored solid line represents the estimated trajectory. The color of the trace changes from blue to red, indicating a gradual increase in the error of the ATE. Each line shows the results of five methods in the same data set, and the first two of each line are the trajectory of VINS-Mono with no loopback and with loopback. The third is the method track of length filtering only in this paper (no loopback), the fourth is the complete method track of this paper (no loopback), and the last is the track of our algorithm with loopback. By comparing the three groups of tracks in Figure 7, it can be observed that the proposed method shows better accuracy and stability in the area where the camera has a large rotation. At the same time, compared with the trajectory of VINS-Mono, the trajectory accuracy of the proposed PLI-VINS in V2_01_easy is improved by 40.9%, then it is improved by 53.7% in V1_01_easy, and finally reaches the highest 63.3% in MH_03_medium. The corresponding increases in PL-VINS were only 32.2%, 61.2% and 48.6%. At the same time, by observing the third and fourth track graphs of each group, it can be found that the proposed method performs well in different indoor environments. By properly merging the adjacent line segments to improve the quality of line segments again, the trajectory accuracy of the camera can be effectively improved. Combined with Table 1 and Figure 6 and the operation of the PLI-VINS in the three scenarios, it is not difficult to find that although V1_01_easy and V2_01_easy are indoor scenarios with relatively single environments and limited ability to describe the structural features of line features, this paper improves the quality of line segments by eliminating redundant line segments and merging lines segments; still achieved good trajectory accuracy. However, the MH_03_medium factory scene has a large number of good structural line segment features, which is very conducive to the PLI-VINS to improve the camera trajectory accuracy by using line features. It also shows that the proposed PLI-VINS performs well in various indoor environments. In terms of root mean square error of absolute trajectory error, as shown in Table 2, the proposed method performs better in almost all EuRoC datasets scenarios.