# Seamless Indoor-Outdoor Localization Through Transition Detection
## 5 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**S10**} \\ \hline  & **Estimated** & **Estimated** & **FLP** & **Estimated** & **GPS** \\ \hline Median & 3.906 & 4.110 & 6.564 & 4.224 & 7.926 \\ Mean & 5.151 & 5.239 & 14.329 & 5.491 & 17.858 \\ Std. & 4.599 & 4.264 & 14.514 & 4.505 & 16.899 \\ \hline  & \multicolumn{4}{c}{**S20**} \\ \hline  & **Estimated** & **Estimated** & **FLP** & **Estimated** & **GPS** \\ \hline Median & 4.870 & 5.269 & 14.637 & 5.367 & 16.462 \\ Mean & 7.345 & 7.873 & 16.813 & 7.982 & 20.618 \\ Std. & 6.242 & 6.405 & 9.822 & 6.538 & 10.578 \\ \hline  & \multicolumn{4}{c}{**S21**} \\ \hline  & **Estimated** & **Estimated** & **FLP** & **Estimated** & **GPS** \\ \hline Median & 4.733 & 6.492 & 6.613 & 7.639 & 7.667 \\ Mean & 6.651 & 8.192 & 7.633 & 9.294 & 9.567 \\ Std. & 5.902 & 6.183 & 5.318 & 6.572 & 8.178 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The median, mean, and standard deviation of position estimation errors over four walking scenario tests at Keumha in meters. Figure 7: Position error distribution of the estimated trajectory at Keumha compared to the GPS and FLP. The accuracy of the state classification is shown in Tables 4-6. The ground truth of the states was decided by the located region (indoor/transition/outdoor) of the ground truth trajectory. The state classification accuracy of the estimated trajectory was evaluated  
Figure 8: Position error distribution of the estimated trajectory at Namsan Green compared to the GPS and FLP. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**S10**} \\ \hline  & **Estimated** & **Estimated** & **FLP** & **Estimated** & **GPS** \\ \hline Median & 3.757 & 6.486 & 11.874 & 7.993 & 14.982 \\ Mean & 7.954 & 10.871 & 16.008 & 12.372 & 19.516 \\ Std. & 9.064 & 9.828 & 11.611 & 10.108 & 16.395 \\ \hline \multicolumn{4}{c}{**S20**} \\ \hline  & **Estimated** & **Estimated** & **FLP** & **Estimated** & **GPS** \\ \hline Median & 5.063 & 5.461 & 14.744 & 5.414 & 22.564 \\ Mean & 9.047 & 9.467 & 21.424 & 10.438 & 33.114 \\ Std. & 10.308 & 10.237 & 19.372 & 11.174 & 34.310 \\ \hline \multicolumn{4}{c}{**S21**} \\ \hline  & **Estimated** & **Estimated** & **FLP** & **Estimated** & **GPS** \\ \hline Median & 8.441 & 10.655 & 11.755 & 13.978 & 10.856 \\ Mean & 10.842 & 14.969 & 19.280 & 17.978 & 17.788 \\ Std. & 9.898 & 10.700 & 18.198 & 10.753 & 15.984 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The median, mean, and standard deviation of position estimation errors over four walking scenario tests at Namsan Green in meters. by comparing the ground truth to the handover strategy's state detection result, and the FLP's accuracy was evaluated by comparing the ground truth to the FLP's located region. The value in the table represents the ratio of the time period to the correct state estimation during the total trial time. The position estimation result from the Maru360 building shows that the proposed positioning method produces better results than Google's FLP and GPS, with smaller position error means and medians over all three devices. The results for the Keumha building show that the proposed method produces better results than the FLP and GPS for S10 and S20, while S21 shows a slightly larger mean error compared to the FLP. This was due to the disadvantageous conditions in this building. The indoor positioning engine randomly assumes the initial direction and requires the behavior of the particle filter to correctly estimate the direction. The area of the indoor region was small and had a square shape, having almost no obstacles or structures, which limited the scale of the position estimation difference of the AI-IMU to that of the RSSL,making it hard for the indoor positioning engine to align the direction. The direction error from the indoor positioning engine not only affects the estimations indoors but also affects the estimation in other areas as well.