# Automated Destination Renewal Process for Location-Based Robot Erands
## 4 Results and Discussion
[12], the approach using morphological segmentation achieved an average accuracy of 88.5 \(\pm\) 9.2%, with a relatively high precision and a lower recall depending on the presence of furniture. Our results, with an accuracy of 84.99 \(\pm\) 6.5%, demonstrate a similar performance in space segmentation using grid maps from CAD drawings. Furthermore, it was confirmed that the morphological segmentation method can be appropriately applied in the proposed process, as it offers a faster segmentation performance and a higher recall rate compared to other methods. On the other hand, in [PERSON] et al. 's study [36], GOOGLE DeepLabV3+ was applied to the floor plans of apartment houses, achieving an inference accuracy of 92.09% for spaces such as living rooms, bedrooms, bathrooms, balconies, and entrances. They emphasize the necessity of having a sufficient amount of data and pre-processing training images for a relatively low accuracy. Therefore, we confirmed that it is important not only to focus on the spatial segmentation process, but also to consider pre-processing for the morphological diversity of spaces and post-processing of the results to improve performance. Figure 8 illustrates the process of adding obstacles while estimating the location using a distance sensor module within a segmented grid map of a single space. Figure 8a shows the grid map before the update, Figure 8b shows the grid map after the update, and Figure 8c displays the actual room image with furniture. The mobile robot simultaneously detected obstacles using a LiDAR sensor (RPLiDAR A3 made by Shanghai Slantec Co., Ltd., Shanghai, China) and a depth camera. For obstacles that are challenging to detect with LiDAR, such as a table or chair with an open bottom, we aimed to update more realistic obstacles through multi-sensor detection using the depth camera. Next, to compare the accuracy of indoor data collection using the space classification module, recognition results were evaluated using 133 images collected from engineering buildings and the places205 dataset. Table 2 represents the classification results using the places205 dataset and the engineering building image set. When examining the top-1 accuracy, both datasets show a recognition performance of not too high, which suggests a high potential for misclassification in spatial distinctions except for some environments. However, subsequent verification through top-5 accuracy revealed an accuracy of 87% or more, indicating a relatively stable performance and probably anticipating the potential for further accuracy improvements through post-processing. \begin{table}
\begin{tabular}{c c} \hline \hline
**Floor** & **Precision** \\ \hline Map\_10F & 85.19\% \\ Map\_2F & 76.92\% \\ Map\_B1F & 92.86\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy of space segmentation for each floor. Figure 8: Results of grid map update using sensed obstacles: (**a**) segmented grid map of a single space, (**b**) grid map after the update, (**c**) actual room image with furniture. Figure 9 presents the results of an experiment on assigning semantic spatial information by equipping each space in the building with furniture and fixtures. The experiment checked that the conference room with desks and chairs (Figure 9a), the hallway with multiple doors and a long passageway (Figure 9b), the pantry with food and beverages (Figure 9c), the office with office monitors and chairs (Figure 9e), and the reception with someone checking entry and exit (Figure 9f) were semantically matched through space classification. In particular, it was also confirmed that spaces with medical beds and IV stands were recognized as hospital rooms (Figure 9d). Figure 10 depicts the results of semantic label assignment experiments conducted on one floor of the building. Spaces were recognized from each of the segmented collected images, and the results were automatically labeled on the map. Compared to [PERSON] et al.