## 5 Experiment and Analysis
### Evaluation of Position Performance  
#### 5.2.1 Evaluation on the Public TUM Dynamic Dataset  
Three dynamic sequences including fr3_walking_xyz, fr3_walking_rpy, and fr3_walking_halfsphere in TUM dataset are used to evaluate the positioning result of Dynamic-DSO. In the three sequences, two pedestrians walk around the table quickly in front of the camera, which is the typical indoor high dynamic scene and has high requirements for the robustness of the direct visual odometry.  
DSO cannot complete initialization in these three dynamic sequences, so it cannot give positioning results. In this paper, after removing the interference of dynamic objects by combining the image semantic segmentation, Dynamic-DSO can initialize successfully in these dynamic sequences, and gives decent positioning results, which improves the practicability and robustness of visual odometry based on direct method in dynamic scenes.  
Figure 8 shows the heat maps of the positioning trajectory of Dynamic-DSO in three sequences, where the gray dotted line is the trajectory groundtruth, the colorful solid line is the estimated trajectory of Dynamic-DSO. The color indicates absolute pose error (APE) for the translation part between the estimated trajectory and groundtruth. The color bar represents that the error gradually increases from the cold color to the warm color. From three heat maps, it can be seen that the trajectory estimated by Dynamic-DSO fits well with the groundtruth (note that in Figure 8b, due to the small motion amplitude of camera, it seems that the deviation between the estimated trajectory and the groundtruth is large, but the maximum error is only 0.129 m).  
Table 1 gives statistics on the APE for the translation part of the Dynamic-DSO and DSO in three TUM fr3_walking dynamic sequences. Since the current published methods are based on indirect method (feature point method), in addition to the comparison with DSO, we also compared the proposed method with several systems based on indirect method in the public TUM dynamic dataset, including SOF-SLAM [3], DS-SLAM [14], Dynamic-SLAM [16] and Semantic SLAM [15]. It is worth noting that since our system is based on a direct method, the positioning accuracy is slightly lower than that of the systems based on the feature point method in the texture-rich dataset such as TUM dynamic dataset, which can be explained in more detail in [31]. However, as the parallel branch to the feature point method in the SLAM and VO field, the direct method has the advantages of better positioning accuracy and system robustness than the feature point method when there are not enough feature points due to the weak texture environment [1, 31], and can construct the semi-dense map.  
Figure 8: Heat maps of the positioning trajectory of Dynamic-DSO in three sequences. (**a**): The estimated trajectory heat map on fr3_walking_xyz. (**b**): The estimated trajectory heat map on fr3_walking_rpy. (**c**): The estimated trajectory heat map on fr3_walking_halfsphere. DSO cannot complete initialization in these three dynamic sequences, so it cannot give positioning results.  
As shown in the table, due to the failed initialization, DSO which is the state of the art system of direct method, cannot output positioning results. It can be seen from the root mean square error (RMSE) that the trajectory error of Dynamic-DSO is less than 6 cm by combining image semantic segmentation, which significantly improves the positioning accuracy and robustness of direct method in dynamic environment.