# Automated Destination Renewal Process for Location-Based Robot Erands
## 4 Results and Discussion  
To validate the proposed two processes, we selected the engineering building of a university, which encompasses various indoor spaces. Specifically, we identified a floor within the building where the actual mobile robot could freely perform errands. Subsequently, we conducted experiments for gathering information on the indoor space through movements and exploration. Figure 6 illustrates the process of transforming a grid map from the CAD drawing of a single-floor building. Figure 6(a) shows the original CAD drawing (Autodesk's AutoCAD 2022 version and DWG format were used), Figure 6(b) displays the image after removing dimension and annotation, and Figure 6(c) presents the image transformed into a grid map. Figure 5: Hierarchical organization based on the connectivity of the indoor environment. Figure 6: Example of grid map creation based on CAD drawing: (**a**) original CAD drawing, (**b**) image after removing dimension and annotation, (**c**) image transformed into a grid map. The step of removing unnecessary annotations during the grid map transformation was conducted partially manually. Due to the varying formats of the drawings and the designer's discretion, it was somewhat challenging for the process to address all the de-tailed aspects. For instance, when working with drawing images, it is crucial to differentiate between guidelines. However, discerning between guidelines used for indicating dimensions and lines representing the building's structure is challenging, as some lines may share similar characteristics such as color or thickness. Additionally, elements like doors, which denote traversable spaces but may visually obstruct parts of the drawing, require additional processing due to the semantic complexity involved in understanding and distinguishing. Hence, the task extends beyond merely recognizing and removing numbers or characters; in the experiments, we discovered that it involves additional subsequent processes to clearly distinguish guidelines from the architectural structure. Although manual intervention was necessary for removing annotation information in this study, in drawings where annotation information is managed in separate layers, we check that this process can be relatively straightforward by simply removing annotations. Figure 7 depicts the results of spatial segmentation within the grid map, carried out based on the CAD drawing. The first column shows the drawing pre-transformation, whereas the second column illustrates the grid map produced from these drawings. As evident from the results, areas where the gap between occupied grid cells is narrower than the surroundings generally correspond to doorways, serving as boundaries. However, in larger spaces or lower floors requiring structural support, spatial divisions are influenced by columns installed for building safety. Additionally, the morphology-based approach may face challenges when boundaries are ambiguous, such as when doors open on both sides or when spatial delineation is unclear. The erosion step should create boundaries by eroding a certain distance from occupied grid cells, but in narrow spaces, this process might result in the entire space being perceived as a single unit. Table 1 presents the accuracy results for Figure 7. The accuracy was assessed by comparing the labeled areas obtained through manual labeling by individuals from the actual drawings with the labeled areas obtained through the segmentation process. As mentioned earlier, in all areas, some narrow spaces are not recognized as a single space in the erosion step, resulting in being treated as the same area as the adjacent space. In the case of the second floor, since most spaces are large and their number is low, it shows a relatively low accuracy, despite the same number of recognition failures. Figure 7: Results of space segmentation for each floor in the engineering building. In [PERSON] et al.