# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 6 Discussion  
### Performance Comparison  
To demonstrate the advantages of our proposed method, we compare it with other approaches in this section. As our primary focus is on applications in real-world scenarios, the comparisons are conducted within such contexts. Dense depth estimation is an effective technique for map construction, as it estimates the depth of obstacles, i.e., the distance between the obstacle and the camera. We compare four depth estimation networks [27, 34, 35, 36], all of which have demonstrated superior performance in recent years.  
As shown in Figure 13, since the original output of the stereo matching network is the disparity of the stereo image, we first converted the output disparity map to a depth map using our own camera parameters. To obtain the depth of the target region, we first load a depth image using the OpenCV library. Depth images are usually single-channel images, where each pixel value represents the depth information of the corresponding point in the scene. However, the information in the depth map is often normalized relative values, so we need to extract the original information in the depth matrix based on the corresponding pixel regions in the depth map so that we can get the real depth values. Using OpenCV's mouse callback mechanism, we implemented a function that allows users to draw any polygonal region on the depth map, generating a binary mask with pixel values of 255 inside the polygon and 0 outside. This enables us to extract the depth values of the corresponding region. Finally, outliers are removed, and the mean value is computed as the final depth of the region. We selected several typical obstacles in the real world and measured their actual distances. Subsequently, we applied the four aforementioned stereo matching algorithms for depth estimation and computed their root mean square errors, as presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**RMSE in Mapping Experiment**} \\ \hline \multirow{2}{*}{**Scene**} & \multirow{2}{*}{**Map Scale (m\({}^{2}\))**} & \multicolumn{3}{c}{**RMSE (m)**} \\ \cline{3-5}  & & **Position x** & **Position y** & **Total** \\ \hline Scene 1 & 900 & 0.367 & 0.404 & 0.386 \\ Scene 2 & 1200 & 0.288 & 0.347 & 0.319 \\ Scene 3 & 2100 & 0.485 & 0.325 & 0.413 \\ Scene 4 & 3000 & 0.387 & 0.378 & 0.382 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The root mean square error (RMSE) of mapping results.  
Figure 13: Obtaining depth information after converting the original image to a depth map.  
Comparison of Tables 1 and 2 shows that our algorithm is competitive compared with other methods. Among them, CroCo-Stereo [34] performs better than our algorithm; however, accuracy is not the only evaluation metrics--the efficiency and speed also needs to be considered. Therefore, we also compared the Graphic Processing Unit (GPU) memory usage and the time required to process one frame of the input image on the RTX 3070.  
As can be seen from Table 3, though CroCo-Stereo performs excellently, it consumes much more resources and time than ours to process one frame. Thanks to the high efficiency of FCN, our method achieves high performance and low consumption and most likely to achieve real-time mapping.