# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 5 Experimental Results
### Experimental Setup and Environment  
The experiments were conducted on a fully self-constructed experimental platform, as shown in Figure 5. The platform uses a four-wheel independent drive and four-wheel independent steering (4 WID-4 WIS) [33] control system, where each of the four wheels is equipped with independent steering and drive motors. A personal computer (PC) functions as the main controller, commanding the platform's movement such as forward, backward, turning, and rotating in place via a mobile app. A stereo camera with a resolution of \(1280\times 720\) is mounted at the front of the platform, with an RTK measurement terminal positioned directly beneath it to determine the vehicle's initial position and orientation, as well as to provide accurate RTK-measured landmark data. The specific details of the experimental setup are provided in Figure 6.  
The experimental environment in real-world scenarios is shown in Figure 7. To ensure a variety of experimental conditions, four locations with different obstacles were selected. As depicted in Figure 7a, square flower beds with sides ranging from 1 to 2 m were chosen as obstacles. Figure 7b shows cylindrical flagpoles with a diameter of approximately 50 cm as obstacles. These two scenarios allow for the evaluation of the algorithm's reconstruction performance on obstacles of varying sizes. Figure 7c,d depict two more complex terrains  
Figure 5: Self-constructed experimental platform. Figure 6: List of experimental equipment. SinoGNSS RTK: Shanghai SinoGNSS Navigation Technology Co., Ltd. (Shanghai, China). MYNTAI Stereo Camera: Shenzhen MYNTAI Technology Co., Ltd. (Shenzhen, China). Lenovo Legion Laptop: Lenovo Group Ltd. (Beijing, China). with minimal obstacles along the road surface. Since the algorithm primarily constructs the grid map by fitting the ground plane, these scenarios were selected to test the algorithm's reconstruction capabilities for road surfaces. During the experiment, the experimental platform was remotely controlled to move at a speed of 0.5 m/s while mapping the environment. When approaching obstacles, the speed was reduced to improve mapping accuracy. Throughout the experiment, true coordinates for every point on the road surface and obstacles could not be obtained. Since the primary application of the algorithm is to use the constructed map for path planning and enable autonomous navigation, it is unnecessary to acquire the true coordinates of all points as ground truth for map evaluation. Instead, the edges of obstacles and the road surface were selected as landmarks. Sampling was performed at these landmarks, and the true coordinates of the sampling points were measured using RTK, serving as ground truth for the map. As shown in Figure 8, the green points represent the center of the free grid. The green areas denote traversable regions, whereas the black areas signify non-traversable regions. The mapping results in Figure 8 exhibit slight visual differences compared to Figures 9c-12c. This discrepancy arises because, in our visualization program, users can zoom in and out on the map using the middle mouse button. At higher zoom levels, discrete green grid centers become visible. However, for larger maps, we scale down the display to present the entire map, making individual grid centers indiscernible and instead forming a continuous green region. The resolution of the constructed grid map is known, with each grid cell measuring 0.1 m in length. Using the initial position coordinates and orientation of the experimental platform, obtained through RTK measurement, the true coordinates of any point in the grid map can be computed through simple calculations. Since the map is two-dimensional, minor errors in the vertical direction are acceptable. Mapping performance is evaluated by calculating the coordinates of selected landmark sampling points in the grid map and comparing them with the true coordinates to determine the error. Figure 7: Experimental scenarios in the real world.