## 5 Discussion
### Synthetic Dataset Evaluation
#### 5.2.1 Floor Map Evaluation
Figure 20: Area deviation between rooms (ADR) of rooms and corridors. Each line represents ADRs in different data. Figure 18: IoU of rooms and corridors Each line represents IoUs in different data. of Synthetic Data-8, which showed that the reconstructed rooms and corridors and ground truth were almost consistent. The results showed the effectiveness and availability of the proposed method for room-space segmentation. Under- or over-segmentation was overcome in this study. Moreover, the calculated DDPs in most rooms were under 15 cm, and some values were below 5 cm. The results showed that points between the floor map and ground-truth data were extremely close. The absolute values of ADR calculations in each floor were below 2 m\({}^{2}\), except for Synthetic Data-4 and the second floor of Synthetic Data-8, which showed that the reconstructed area in the floor plan and ground-truth area were similar. All of the experiments showed the robustness and capability of the proposed method for floor map reconstruction. The IoUs of all the corridors, including straight corridors (Synthetic Data-1), ring-shaped corridors (Synthetic Data-3, -5, and -6), and L-shaped corridors (Synthetic Data-2), were above 95%, except the second floor of Synthetic Data-4, which indicates that under or over-segmentation in long corridors was overcome by using the proposed method. The detected corridors were nearly consistent with the ground-truth data. The DDPs of all the corridors were less than 15 cm, and the added uniform noise was 5 cm. Furthermore, the absolute deviation of the area in each corridor was less than 1.5 m\({}^{2}\), except Synthetic Data-4. All of the experiments showed the robustness and availability of the proposed method for corridor detection and reconstruction. According to the evaluation of Synthetic Data-4 in Table 4, curved walls and round rooms could not be correctly reconstructed, as shown in row 4 in Figure 16c. Instead, a curve was detected as a set of lines along the curve because our method is designed for wall-line extraction and considers a curve to be a set of lines. Thus, the proposed method can only address indoor interiors with perpendicular walls and not curved walls. According to the evaluation of Synthetic Data-6, the proposed method was robust in terms of floor map reconstruction with vertical walls, which were parallel to the gravity vector. However, clutter and occlusion, especially in the corners, affected the level of detail in the reconstruction without altering the coarse structure, according to the evaluation of the first floor in Synthetic Data-7 in Table 4. A small amount of clutter and occlusion slightly affects small details. With more clutter, fewer details may be reconstructed, although the coarse structure could be correctly recovered, as shown in row 7 in Figure 16c. For partially scanned rooms, some walls were not sampled at all, especially in the corners in Synthetic Data-7. Thus, these missing data may have hampered the extraction of wall lines, and some walls could not be detected. According to the performance on the first floor of Synthetic Data-8 in Table 4, the missing data in the rooms could be handled by the proposed method because the labels of the cells in the room were determined by the overlapping and interior non-value cells, which were surrounded by the same labeled cells, and were thus labeled by the neighbors' label. However, the missing data in the wall area may have hampered the reconstructed floor map, as shown in the second floor of the evaluation of Synthetic Data-8. This result occurred because the missing data in the walls prevented the extraction of certain walls. Thus, the floor map of the second floor of Synthetic Data-8 could not be adequately reconstructed, as shown in row 8 in Figure 16c. According to Figure 18, the IoU of room 31 in Synthetic Data-8 was below 75%, and the ADR was approximately 7 m\({}^{2}\).