## 5 Experiment and Analysis
### Evaluation of Position Performance
#### 5.2.2 Evaluation on the Modified Euroc Dynamic Dataset  
The existing public indoor small-scale dynamic dataset is relatively small, we have conducted the comparative experiment in the public TUM indoor dynamic dataset. To further evaluate the performance of the proposed method and DSO in the indoor small-scale dynamic scenes, we inserted dynamic objects into the static Euroc indoor dataset. In the public Euroc dataset, each image is matched with a high-precision pose groundtruth obtained from the professional optical motion capture system, so after inserting the dynamic object into Euroc dataset, we can get the dynamic image sequence matching the high-precision pose groundtruth. Then we can make quantitative analysis by comparing the estimated results with the groundtruth. * Data Preprocessing  
The Euroc dataset [30] is collected in static scene. The collection frequency is 20 Hz, and the resolution is 752 \(\times\) 480. Each image matches the high-precision groundtruth provided by the motion capture system. We consider moving pedestrian as the noise, which is artificially synthesized into the Euroc image sequences. The image sequences are changed from a static scene to a dynamic scene after adding dynamic objects. As shown in Figure 9, the device that records the dynamic pedestrian video sequence is an Intel ZR300 camera containing a color global shutter camera, and a laptop with Intel Core i7-7000 CPU and 8 GB memory. Firstly, we fix the acquisition device on the indoor desktop, and a person walks around as dynamic object in front of the camera. A video sequence containing the dynamic pedestrian is recorded at the same frequency and image resolution as the Euroc dataset. Then, the dynamic pedestrian in each frame is segmented by using Mask-RCNN [25]. Finally, the segmented continuous moving pedestrian is added to each image in the Euroc image sequence to synthesize the Euroc dynamic scene dataset. We add dynamic objects to the image sequences in the Euroc dataset including V1_01, V2_01 and V2_02, and record the names of these modified sequences as V101_syn, V201_syn and V202_syn. By using V101_syn, V201_syn and V202_syn, the performance of DSO and Dynamic-DSO can be evaluated in dynamic scenes. Figure 10 shows the processing of the V202_syn sequence. Figure 10a shows images in the original V2_02 sequence. It can be seen that these images do not contain any moving objects. Figure 10b shows images collected by ourselves in the indoor environment, which contain a moving pedestrian. Mask-RCNN is used to segment the pedestrians in Figure 10b, and the result of segmentation is shown in Figure 10c. By using the semantic image as mask, the pedestrian in Figure 10b is extracted and synthesized into the V2_02 sequence in Figure 10a, which generates the synthetic V202_syn sequence shown in Figure 10d. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**High Dynamic Sequence**} & \multicolumn{2}{c}{**Dynamic-SLAM**} & \multicolumn{2}{c}{**SOF-SLAM**} & \multicolumn{2}{c}{**DS-SLAM**} & \multicolumn{2}{c}{**Semantic**} & \multirow{2}{*}{**DSO**[11]} & \multicolumn{2}{c}{**Dynamic-DSO**} \\ \cline{2-2} \cline{4-11}  & [16] & [3] & [14] & & **SLAM**[15] & & & \multicolumn{2}{c}{**Dynamic-DSO**} \\ \cline{2-11}  & **Rmse** & **Rmse** & **Rmse** & **Rmse** & **Rmse** & **Mean** & **Std** & **Rmse** & **Mean** & **Std** \\ \hline f3\_walking\_xyz & 0.013 & 0.018 & 0.025 & 0.034 & \(\backslash\) & \(\backslash\) & \(\backslash\) & 0.032 & 0.033 & 0.011 \\ f3\_walking\_rpy & 0.060 & 0.027 & 0.444 & - & \(\backslash\) & \(\backslash\) & \(\backslash\) & 0.061 & 0.062 & 0.034 \\ f3\_walking\_half & 0.021 & 0.029 & 0.030 & 0.064 & \(\backslash\) & \(\backslash\) & \(\backslash\) & 0.063 & 0.055 & 0.042 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of absolute Pose Error (APE) for translation part in public TUM dynamic dataset. (Unit: m).