# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.9 Segmentation
It could be summarized that the algorithms described in the work are now the \"classic\" ones and are often used in the preprocessing phases of machine-learning-based approaches. [PERSON] and [PERSON] [147], in 2016, presented one of the first CNN-focused pieces of research on grid map segmentation. Although their network was trained to recognize only three distinctive classes (rooms, corridors, doorways), the presented architecture combining just two sets of convolutional and pooling layers with three fully connected layers was reported to implement the task successfully. [PERSON] and [PERSON] [197], in 2018, presented an article focused on the construction of intelligent service robots. Their hybrid approach to semantic mapping combined two sources of data. The first one was an occupancy grid map from the robot's laser rangefinders, spatially segmented with a distance transformation. The second one was a topological map, created from pictures taken by the robot and searched for objects with the use of a CNN. Te combined results created a set of meaningful, spatially segmented topological nodes, classified by a probabilistic Bayesian Classifier, resulting in a generated semantic map. In 2018, [PERSON] et al. [135] presented a novel map segmentation method based on distance transformation. Their solution was reported to achieve better results than the referenced methodology of [PERSON] et al. [1]. In the proposed process, an algorithmic convolution of a circular kernel on a distance map was used to generate an extended set of region proposals. Such an over-segmented map was later merged into larger, straightened areas, representing rooms. The same test dataset was used by [PERSON] et al. [136] in 2019. They moved from a pure algorithmic to a learning-based approach. The proposed approach utilized a combination of a CNN and a segmentation network to generate the door-region hypotheses, later checked with computer vision algorithms. Based on the knowledge of where the doors were located, a segment classification into rooms and corridors was performed. A year later, in 2019, [PERSON] et al. [50] presented a complex semantic mapping framework. In their scenario, an RGBD image was processed in three different ways and then connected to create a single result. Simultaneously, RGB images of the environment were searched for objects using a CNN, and depth data were combined with laser radar measurements to create a 2D semantic map. The acquired 3D space map was cast to a 2D navigation map and merged with the semantic map to create scene labels. In 2020, [PERSON] et al. [140] presented a team of robots performing semantic mapping in a supervised learning approach. Their main focus was introducing P2P communication between robots and omitting the resource limitation problem. After that, they investigated the map segmentation task. Their high-level algorithm relied on initial preprocessing of the grid map to reduce noise and simplify a Voronoi Graph Map generation. Specific features of such a map were extracted and integrated into a classification algorithm. In the article, custom neural network- and SVM-based approaches were tested, with the SVM solution reported to return slightly better results. One of the newest articles, by [PERSON] et al. [198], presented a fused approach that combined scene recognition with object detection. Similarly to other multi-network architectures, they implemented subtasks separately and later fused the results. In this case, the EfficientNet-B2 [216] network was used for the initial classification of the scene, and the YOLOv5 model for the detection of objects. The proposed network added two fully connected layers processing the merged output of subnetworks. Additional room segmentation with MAORIS [135] and a combination of results led to obtaining a semantic metric map. In 2021, [PERSON] et al. [141] presented a system usable on low computing power devices like the Raspberry Pi.