# Seamless Indoor-Outdoor Localization Through Transition Detection
## 5 Experimental Results  
The test was conducted in three office buildings (indoor area: Maru360--140 m\({}^{2}\), Keumha--220 m\({}^{2}\), and Namsan Green--470 m\({}^{2}\)) with three different kinds of smartphones (Samsung Galaxy S10, S20, and S21). To train the positioning model, 30 min was spent in collecting the unlabelled datasets by two people with two smartphones each (three Samsung Galaxy S21s and one Samsung Galaxy S20). The test trial was conducted by one person, and the same trajectory was repeated for three (Keumha, Namsan Green)/four (Maru360) times. The black line shows the ground truth trajectory, while the green, blue, and red lines each refer to the estimated trajectory from the proposed method, Google's Fused Location Provider (FLP) algorithm [19], and GPS. For ground truth generation, the initial trajectory was estimated using the AI-IMU method. This trajectory was then refined by interpolating between the designated positions such as the start and end pointswithin the time domain to produce a continuous ground truth path. Figures 3-5 show examples of the experimental results compared with Google's fused location provider API for each building. The position errors were compared between the estimated trajectory, FLP, and GPS for each device. The distribution of errors is shown with box plots in Figures 6-8, the box plot in the left column shows the distribution of the position errors during all trials, the box plot in the center column shows the errors between the FLP position measurement and the estimated trajectory, and the box plot in the right column shows the errors between the GPS position measurements and the estimated trajectory. The estimated trajectory's position error compared to the FLP error was calculated by interpolating the trajectory by the FLP measurement's timestamp and then comparing it with the ground truth. Similarly, the estimated trajectory's error compared to the GPS error was calculated by interpolating it with the GPS measurement's timestamp and then comparing it with the ground truth. Figure 3: Estimated trajectories compared to a GPS and Google’s FLP at Maru360. The median, mean, and standard deviation of the errors shown in the plot are also summarized in Tables 1-3. Figure 4: Estimated trajectories compared to the GPS and Google’s FLP at Keumha. Figure 5: Estimated trajectories compared to the GPS and Google’s FLP at Namsan Green. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**S10**} \\ \hline  & **Estimated** & **Estimated** & **FLP** & **Estimated** & **GPS** \\ \hline Median & 4.562 & 7.426 & 10.788 & 8.573 & 11.118 \\ Mean & 5.792 & 6.959 & 13.576 & 7.706 & 17.387 \\ Std. & 4.562 & 4.300 & 8.904 & 4.230 & 13.864 \\ \hline  & \multicolumn{4}{c}{**S20**} \\ \hline  & **Estimated** & **Estimated** & **FLP** & **Estimated** & **GPS** \\ \hline Median & 2.607 & 2.652 & 7.852 & 2.787 & 6.695 \\ Mean & 3.259 & 3.200 & 23.807 & 3.278 & 13.015 \\ Std. & 2.335 & 2.065 & 78.280 & 2.133 & 13.301 \\ \hline  & \multicolumn{4}{c}{**S21**} \\ \hline  & **Estimated** & **Estimated** & **FLP** & **Estimated** & **GPS** \\ \hline Median & 4.828 & 11.137 & 13.654 & 11.321 & 13.199 \\ Mean & 6.946 & 10.357 & 17.957 & 10.483 & 17.278 \\ Std. & 5.568 & 4.841 & 9.523 & 5.014 & 8.876 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The median, mean and standard deviation of the position estimation errors over four walking scenario tests at Maru360 in meters. Figure 6: Position error distribution of the estimated trajectory at Maru360 compared to the GPS and FLP.