# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.9 Segmentation
The main segmentation logic was based on the clustering process of the grid map's subparts (\"faces\"). Segments created by a set of processing operations (Hough transformation, contouring, wall identification, and representative line creation) were later joined together to build entire rooms. The final layout was the result of the detected room features' analysis. The newest publication [142], from 2022, indicates how much the approach evolved over a decade of research. The presented system, called Robust Structure identification and Room Segmentation (ROSE2), was reported to be capable of segmenting and identifying rooms even from a partial, heavily cluttered indoor environment's occupancy grid map. In 2012, [PERSON] [144] published a semantic map segmentation prototype based on the idea of room function. Instead of focusing on the shape of the room, the main focus was on what the area is used for. The presented algorithm implemented the task using an energy maximization approach, where the energy function defined how well a tuple of room, label, and relational index described the space analyzed. In one of the first realizations, [PERSON] et al. [127] presented research on semantic labeling using two elements: Non-negative Matrix Factorization (NMF) and Generalized Learning Vector Quantization (GLVQ). In their solution, a set of basic primitives and activities was achieved with NMF and used to create histograms of environment characteristics. The distance transformation applied to the combined results encoded the representation into a vector space. Finally, the GLVQ was used to predict the class labels for each primitive analyzed. [PERSON] et al. studied the text descriptions used in semantic labeling. An article [192] from 2014 presented a continuation of their previous work [206]. In the original version, a robot learned the environment representation in a guided tour from grid maps and textual descriptions. The discovered environment was decomposed into uniformly sized regions and represented using a semantic graph. In the later work, the inter-region connections were additionally analyzed and the scene classification factors were extended. The introduced use of the robot's camera and laser  
Figure 13: Visualization of the Segmentation task. An input image (**left**) is searched for separated areas representing individual rooms. Each room is marked with a polygon in a different color (**right**). rangefinders allowed for area-type reasoning, even without textual descriptions. In 2015, [PERSON] et al. [145] presented a solution nowadays described as relatively simple. They proposed detecting walls in a grid map and using them as lines to create a matrix-like grid, where each adjacent cell was later merged with the use of expert-provided knowledge or the watershed algorithm. Also in 2015, [PERSON] et al. [146] published an article documenting their research of the Generalized Voronoi Graph (GVG) application to an incrementally generated topological segmentation of an environment. In their solution, an environment-scanning robot explored an unknown area and constructed a GVG. The post-processing step clustered the initially generated, fine-grained set of areas into larger room-like structures. In the same year, [PERSON] et al. [193] used a set of cameras and sensors placed on a robot platform exploring the environment to deduce the category of place and its meaning in a four-step procedure. The algorithm first used a CNN to classify taken photos, then classifiers to detect new scene classes, and finally a Bayesian filter and a mapping subsystem to create the resulting place labels. We also considered the very initial research article by [PERSON] et al. [1] for the segmentation subtask. They provided not only a review of used techniques but also their basic implementations. They compared four different approaches based on morphological operations, distance transformation, Voronoi Graph, and features processing. Their work was commonly referenced in later publications, and the analyzed techniques were widely used.