## 2 Related Work
The space is first decomposed into cells by means of extracted wall lines [26], and then the label of each cell is confirmed by an energy minimization method. However, such an operation can lead to over-segmentation of rooms, especially for long corridors. Door extraction has also received a lot of attention in recent years. Since the indoor environment contains many obscured objects, resulting in a large number of missing walls, it is of great nuisance to perform opening detection. [PERSON] et al. [2] introduced an approach to identify wall occlusions and opening detection. The approach marks the occluded part by ray tracing and then identifies the openings by support vector machine. [PERSON] et al. [27] formulated the wall features extraction as a graph-cut optimization problem on a 2D cell complex defined by the line features of the reconstructed wall surface. Similar to the approach of [PERSON] et al., this study also required the location of the laser scanner as a prerequisite for complementation. Other than that, [PERSON] et al. [10] used the point cloud with color information to identify closed and half-open doors. To collect point cloud with color information, an extra camera is needed and calibration between the camera and lidar is required. In addition to this, some related studies working on indoor map construction and context labeling for indoor localization and navigation are ongoing. In terms of constructing indoor maps, [PERSON] et al. [28] presented a method to generate indoor floor plans based on WI-FI fingerprints. Similarly, [PERSON] et al. [29] proposed the new ray-aided generative adversarial model to automatically construct the radio map, which is used for the indoor WLAN intelligent target intrusion sensing and localization. [PERSON] et al. [30] constructed the indoor navigation network based on floor plans and walking habits. In terms of context labeling, some work on semantic segmentation of indoor environments is developing [31; 32; 33; 34]. [PERSON] et al. [10] construct indoor navigation networks based on indoor semantic information. From the above analysis, there have been many research results on information extraction about navigation elements, but most of the methods have limitations in obtaining indoor navigation element information. 1. For boundary extraction, the outlines of point cloud horizontal projection can be affected by the divergence data at the openings. The wall segments obtained from the wall projection are usually not able to provide the complete boundary outline information due to occlusions. Therefore, a single method usually does not provide good boundary due to the occlusions and local divergence data. 2. For room segmentation, multiple building elements on multiple floors can be effectively segmented based on trajectory lines and viewpoints, but this method usually fails when faced with data without trajectory lines and viewpoints. The existing segmentation methods that do not rely on trajectory or viewpoint information suffer from under-segmentation or over-segmentation. The connecting transition space between rooms, as an important element of the indoor space, is rarely discussed in the literature. 3. For door extraction, in cluttered indoor environments, the current method usually uses viewpoint information to project the occlusions in the room onto the wall to restore walls. In the absence of viewpoint information, walls that are heavily influenced by occlusions are usually unable to accurately extract door information. Secondly, there are some rooms with closed doors during data collection. Furthermore, it is still a challenge to detect closed doors in the point cloud without color information.