# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 3 Experiments and Results
### Shortest Path Planning  
To verify the spatial analysis capability of the extracted indoor navigation network, we applied the [PERSON] algorithm [37] for the shortest path planning computation with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Scene** & \begin{tabular}{c} **Input File** \\ **Size (MB)** \\ \end{tabular} & \begin{tabular}{c} **Output File** \\ **Size (KB)** \\ \end{tabular} & \begin{tabular}{c} **Average Runtime \({}^{*}\)** \\ **(Seconds)** \\ \end{tabular} \\ \hline S3 DIS & \begin{tabular}{c} Area\_1 \\ Area\_4 \\ Area\_6 \\ \end{tabular} & \begin{tabular}{c} 3534.0 \\ 2795.5 \\ 2693.1 \\ \end{tabular} & \begin{tabular}{c} 5344.0 \\ 355.1 \\ 57 \\ \end{tabular} & \begin{tabular}{c} 344.0 \\ 322.0 \\ \end{tabular} \\ \hline L501 & \begin{tabular}{c} F0 \\ F1 \\ F2 \\ F3 \\ F4 \\ \end{tabular} & \begin{tabular}{c} 13.7 \\ 19.0 \\ 15.0 \\ 21.4 \\ \end{tabular} & \
\begin{tabular}{c} 19 \\ 23 \\ 24 \\ \end{tabular} &
\begin{tabular}{c} 3.9 \\ 4.2 \\ 3.7 \\ 4.0 \\ 4.8 \\ \end{tabular} \\ \hline \hline \end{tabular} \({}^{*}\)The average runtime across the five executions.  
\end{table}
Table 2: Input file size, output file size, and runtime statistics for navigation network extraction across different datasets.  
Figure 15: The results of the extracted indoor navigation network with self-collected dataset: (**a**) floor 0, (**b**) floor 1, (**c**) floor 2, and (**d**) floor 3, (**e**) floor 4.  
each generated navigation network. By inputting the IDs of the start and end nodes, along with a parameter indicating whether wheelchair access is required, we can compute the shortest path connecting the start and end points within the network. This method not only verifies the accuracy of the navigation network, but also evaluates its potential in real-world applications, particularly for accessible path planning for people with mobility impairments.  
Figure 16 shows the shortest path planning results for the single floor navigation network extracted from the S3 DIS dataset. For the multi-floor navigation networks in the self-collected dataset, the shortest path planning results across floors are shown in Figure 17. In these figures, the start and end points are clearly marked, and the path planning results are displayed, starting from purple and ending in red, to visually present the start and end of the path.  
To validate the wheelchair-accessible path planning capability, we conducted experiments on the self-collected dataset. We selected the door node at the entrance on the ground floor as the starting point and the door at the end of the hallway on the fourth floor as the end point. Due to scene constraints, the shortest path always includes the elevator, as shown in Figure 17a. To verify the effectiveness of the accessibility parameter settings, we assumed that a door node leading into the hallway on the fourth floor is not wheelchair accessible, changing its accessibility from true to false, as marked in Figure 17a,b. In this case, the wheelchair-accessible path planning results are shown in Figure 17b, where the  
Figure 16: The results of the shortest path planning for the single floor navigation network extracted from the S3 DIS dataset: (**a**) Area_1; (**b**) Area_4; (**c**) Area_6. The route starts at purple and ends at red.  
path was rerouted to another accessible route on the fourth floor to reach the end point. This test demonstrates the effectiveness of our extracted indoor navigation network in wheelchair-accessible path planning. Figure 17c shows the shortest path planning result when the condition is limited to staircase use only.