# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 5 Experimental Results
### Experimental Results Analysis
#### 5.2.2 Evaluation of Road Surface Reconstruction in the Scene  
As shown in Figure 11a,b, a rectangular road segment, approximately 5 m wide and 130 m long, is selected as the test scene. The green area in Figure 11c represents the road surface in the map. It is evident that, aside from a small section of black error regions in the middle of the road, the road surface reconstruction is generally successful. The red-marked  
Figure 10: Mapping results for Scene 2. (**a**,**b**) The real environment photographs, (**c**) the visualization of the OGM mapping results, (**d**) the ground truth of landmark points, (**e**) the comparison between the sampled points and the ground truth, and (**f**) the errors in the x-axis and y-axis for mapping results.  
areas in Figure 11c indicate sections where our mapping method incorrectly estimates the road's width. Our analysis suggests that this error primarily results from the relatively low steps at both ends of the road and sparse grass coverage in these areas. Consequently, the semantic segmentation fails to clearly distinguish between the lawn and the road, leading to an inaccurate reconstruction of this section. The yellow-marked areas in Figure 11c correspond to three bicycles parked in this location, and the black regions in this part are not mapping errors.  
To assess the stability of the method, a more complex scene was selected to evaluate the reconstruction of the road surface. Unlike the previous scene, this scenario features a circular road within a park, bordered by stone steps and a lawn. The road width is approximately 3.5 m, with a total length of about 100 m. As shown in Figure 12c, the  
Figure 11: Mapping results for Scene 3. (**a**,**b**) The real environment photographs, (**c**) the visualization of the OGM mapping results, (**d**) the ground truth of landmark points, (**e**) the comparison between the sampled points and the ground truth, and (**f**) the errors in the x-axis and y-axis for mapping results.  
road surface was successfully reconstructed, with the exception of the areas marked in red. These regions, located at the boundary between the lawn and the road, are where semantic segmentation fails to generate accurate semantic masks, leading to some lawn areas being mistakenly classified as road surface. Landmark points were selected along the inner and outer edges of the road, including the apex, corners, and junctions, as easily identifiable reference points. Figure 12e illustrates the discrepancies between the positions of these landmark points in the map and their actual locations. As shown in Figure 12f, the majority of the landmark point errors are within 0.5 m. The data in Table 1 represent the root mean square error (RMSE) of our mapping method across four distinct scenes.  
Figure 12: Mapping results for Scene 4. (**a,b**) The real environment photographs, (**c**) the visualization of the OGM mapping results, (**d**) the ground truth of landmark points, (**e**) the comparison between the sampled points and the ground truth, and (**f**) the errors in the x-axis and y-axis for mapping results.  
As shown in Table 1, our method exhibits strong performance across all four scenes, with the RMSE ranging from 0.3 to 0.4 m in each case. Furthermore, during the experiments, our system operates at 30 frames per second, which largely satisfies the requirements for real-time mapping.