# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 4 Experimental Results and Analysis
### Accuracy and Robustness Performance  
We implemented the above methods on the EuRoC datasets. In order to intuitively reflect the tracking effect of our proposed semi-direct method, and because PL-VIO lacks the loop-closure module, we disabled the loop-closure module of the above methods. A comparison of root-mean-square error (RMSE) on the EuRoC datasets is shown in Table 1, and their histograms are also provided, as shown in Figure 10. Table 1 shows that SDPL-VIO works robustly and accurately, and other methods also work well. Compared with point features, the combination of point and line features can strengthen the constraints between images, are insensitive to illumination changing environment, deal with the dynamic environment, and reduce the error caused by fast rotation motion. Thus, due to the introduction of line features, the accuracy of SDPL-VIO is better than SVO, PCSD-VIO and VINS-Mono, which only uses point features for tracking. In easy sequences such as MH01 and V101, which have rich features, good illumination, and slow motion, the feature-based method can extract a high number of point and line features. In such environments, the quality of tracking is about the same, so the accuracy of SDPL-VIO is comparable to PL-VIO. In difficult sequences, such as MH05, V203 with motion blur, fast motion, low texture, etc., the feature-based method faces challenges due to the lack of sufficient visual features. Our proposed semi-direct method combines the excellent performance of direct methods in low-texture environments. The feature-based method provides an accurate initialization state and generates keyframes that provide good priors for the direct method, while the direct method uses direct image alignment to move the frame very close to its final pose, while using a refinement step to reduce the pose estimation error. Therefore, SDPL-VIO performs relatively well in difficult sequences. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sequence** & **SDPL-VIO** & **PCSD-VIO** & **PL-VIO** & **VINS-Mono** & **SVO** \\ \hline MH01 & 0.13 & 0.16 & 0.14 & 0.16 & 0.17 \\ MH02 & 0.15 & 0.17 & 0.15 & 0.19 & 0.27 \\ MH03 & 0.18 & 0.22 & 0.19 & 0.20 & 0.43 \\ MH04 & 0.31 & 0.35 & 0.33 & 0.36 & 1.36 \\ MH05 & 0.23 & 0.28 & 0.25 & 0.30 & 0.51 \\ V101 & 0.06 & 0.10 & 0.07 & 0.09 & 0.20 \\ V102 & 0.10 & 0.11 & 0.08 & 0.11 & 0.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RMSE (m) of the five methods. Figure 11 shows a comparison of the trajectories obtained by SDPL-VIO, PCSD-VIO, PL-VIO and VINS-Mono on the MH01, MH05 and V202 datasets, respectively. The ground truth is represented by the red line, while the results of SDPL-VIO, PCSD-VIO, PL-VIO and VINS-Mono are represented by the blue line, the green line, the purple line and the yellow line, respectively. Figure 12 shows the translation errors on the MH01, MH05 and V202 datasets, in which the blue represents SDPL-VIO, the green represents PCSD-VIO, the purple represents PL-VIO, and the yellow represents VINS-Mono. It can been seen that, in the MH01 sequence, due to the rich scene features and good illumination conditions, there is not much difference in the accuracy of the four methods. The maximum error of SDPL-VIO is 0.37 m in the sequence of MH01, while that of PCSD-VIO is 0.41 m, that of PL-VIO is 0.41 m and that of VINS-Mono is 0.40 m, respectively. The MH05 dataset includes fast motion and large illumination changes. The combination of point and line features is more robust to fast rotation in the trajectory, and the semi-direct method is more adaptable to low-texture environments, so SDPL-VIO has the highest accuracy. PCSD-VIO and VINS-Mono only extract point features, and they struggle to extract corner points with large grayscale differences from surrounding pixel blocks. Therefore, the number of effective feature points is reduced, and the accuracy is the lower than the other two methods.