## 6 Experimental Results
### Global Orientation Optimization  
We compared our semantic-structure-based method (semantic-aware) with three other methods, ORB feature-point-based method (ORB-based), primary-edge-based method (primary-edge-based), and wheel-speedometer-based method (wheel-speedometer-based). In Figure 7, we show the comparisons of the orientation estimation errors for the different methods. As the trajectory becomes longer, the orientation angle error of the three comparison methods gradually increases. The main reason is that errors accumulate over time. However, our method achieves a global estimation of the change in direction and avoids the occurrence of accumulated errors. Therefore, the orientation error of this algorithm is independent of the length of the trajectory. When the vehicle turns, the orientation error of all methods increases significantly. This is mainly caused by blurry images caused by fast rotation. Table 2 shows a comparison of the mean orientation estimation errors of the different methods. The average orientation estimation error of the methods presented in this work is stable between 1 and 2 degrees, well below other methods, both indoors and outdoors. This also verifies that, among the different types of feature information, the structure line information is the most robust to the interference factors, and the orientation results estimated based on it are the most accurate.  
The comparisons of the trajectories for the different methods are shown in Figure 8. Since the proposed method has the most-accurate orientation estimation, the trajectory drift value of the proposed method is much smaller than that of other methods. The trajectory estimated using other information has a significant drift. At the same time,  
Figure 6: The comparison of different edge segmentation methods. Each row represents the results for different structure extraction methods for the same synthesized image, which is illustrated in the first column, and each column represents the results for different synthesized images with the same structure information extraction method, which is described at the bottom of each column. The first column denotes the three input images, the second column the ground truth of edge information, the third column the edge information extracted by the ray accumulation method, the fourth column the edge information extracted by the line segment detection method, and the fifth column the edge information extracted by polyline approximation method. The manually labeled ground truth edges are drawn in green. For each column of segmented edges, the green lines denote that the edges are correctly preserved, while the red lines denote that the edges are not correctly preserved, and the white lines denote those missed for the specific method. Moreover, The color intensity means whether the edges are inside the free space (brighter) or on the contour of the free space (darker).  
it can be seen from the trajectory comparison chart that, although the trajectory using structural information is more consistent with the true value in the upward direction, there are also jagged oscillations and even mutations in some local areas. This is actually due to environmental factors: when there are few marked lines on the road surface in the environment and when the line features are not accurate enough due to blurry images, the estimated dominant direction will oscillate, rather than be smooth enough. Therefore, although the positioning strategy based on structural information proposed in this paper performs well in orientation estimation, the overall trajectory still has errors compared with the truth trajectory.  
Figure 8: The comparisons of the trajectory estimation for different methods. (**a**) Indoor Scene, (**b**) Outdoor Scene..  
Figure 7: The comparisons of the orientation estimation errors of different methods.