# Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles
## 4 Experiments
### Real-World Experiment  
In order to further validate the effectiveness of our proposed method, we conduct a real-world experiment with a ground vehicle. Based on the prior topological map, the ground vehicle will explore an indoor corridor of size \(55\times 15\times 2\) m\({}^{3}\), and a cross-sectional length of road that is 2 m, as shown in Figure 12. The prior information we provided is a rectangular topological map that outlines the basic structure of the indoor corridor. In the indoor corridor, the open spaces and nooks served as regions beyond prior information, testing the effectiveness of our method. In the experiment, we run VINS-Fusion on GPU to provide the positional state, while the proposed method runs on CPU to plan an exploration path [32]. We set \(v_{max}=0.5\) m/s, \(\omega_{max}=0.8\) rad/s, the maximum acceleration as \(0.5\) m/s\({}^{2}\), and the grid map of local update range as \(4\times 4\times 2\) m\({}^{3}\). The FOV of the depth camera is set as \([80\times 60]\) deg, and the maximum range \(h_{max}\) is 3.5 m.  
Figure 13 shows the exploration trajectory of our method with the indoor environment, where the small labeled images represent the key nodes during exploration: (a) presents the robot exploring in the direction following a global exploration strategy; (b) and (c) \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scene**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Exploration Time (s)**} & \multicolumn{4}{c}{**Flight Distance (m)**} \\ \cline{3-10}  & & **Avg** & **Std** & **Max** & **Min** & **Avg** & **Std** & **Max** & **Min** \\ \hline \multirow{4}{*}{Maze-1} & FUEL & 826.599 & 25.275 & 849.384 & 777.630 & 618.773 & 16.821 & 635.879 & 589.081 \\  & FAEP & 848.614 & 32.779 & 906.516 & 813.997 & 605.419 & 28.813 & 655.931 & 561.852 \\  & Ours & 671.101 & 13.880 & 694.328 & 663.532 & 501.523 & 12.490 & 520.579 & 486.109 \\  & Ours\({}^{\text{Local}}\) & 826.881 & 10.485 & 855.695 & 814.620 & 616.217 & 8.390 & 635.330 & 605.435 \\  & Ours\({}^{\text{Global}}\) & 670.742 & 24.009 & 693.757 & 626.820 & 497.563 & 16.467 & 517.316 & 469.384 \\ \hline \multirow{4}{*}{Maze-2} & FUEL & 1407.048 & 46.729 & 1503.542 & 1356.909 & 1012.588 & 18.818 & 1036.984 & 984.151 \\  & FAEP & 1474.686 & 67.125 & 1569.142 & 1418.180 & 913.552 & 33.502 & 960.631 & 885.397 \\ \cline{1-1}  & Ours & 1153.677 & 29.733 & 1200.470 & 1104.911 & 851.646 & 22.824 & 885.435 & 819.141 \\ \cline{1-1}  & Ours\({}^{\text{Local}}\) & 1396.996 & 27.841 & 1439.908 & 1339.974 & 1002.997 & 18.952 & 1037.262 & 980.147 \\ \cline{1-1}  & Ours\({}^{\text{Global}}\) & 1210.028 & 52.735 & 1277.481 & 1138.502 & 886.096 & 41.552 & 940.927 & 831.196 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Exploration statistics in Maze-1 and Maze-2. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scene**} & \multicolumn{4}{c}{**Average Computation Time (ms)**} \\ \cline{2-4}  & **Global Planning** & **Local Planning** & **Total Planning** \\ \hline Maze-1 & 141.79 & 17.39 & 159.19 \\ Maze-2 & 127.45 & 12.35 & 130.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average computation time of each module. present the instances where the vehicle prioritizes exploration directions beyond the prior information. The exploration time of the whole process is 738 s, and the movement distance is 167 m. It can be seen from the trajectory that the vehicle did not revisit the explored areas during exploration, which proves the effectiveness of our proposed method. Figure 14, respectively, shows the process of volume coverage and the number of remaining frontier clusters during exploration. It reveals a stable and linear exploration process while the number of frontier clusters is maintained at a small level, demonstrating the efficiency of our proposed method. Figure 12: The experimental scene for real-world experiment. Figure 13: The exploration trajectory of the proposed method in real-world experiment.