# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 9 Summary  
Our literature review shows that the topic of room segmentation and its semantic meaning retrieval is a fresh area of research, dynamically developing new solutions relying on various data representations. In the fully automated approach to manufacturing processes, it is crucial to have a complete understanding of owned assets, especially in the form of digitalized documentation and virtual models that can be processed by an AI-based system. We have to know what data is already available or can be easily acquired, and how we can utilize it. Performed analysis of the existing literature with the SLR methodology allowed us to identify three perspectives of dwelling on the existing works, called taxonomies. The first one (Section 3) answered the question dedicated to available input data structures. Four different types of data were found to be utilized: 2D images, 3D spatial data, graph structures, and feature sets. The second taxonomy (Section 4) presented the category of processing methods. We specified four such categories: three types of segmentation (with increasingly more precise semantic room understanding) and plain classification itself. The third taxonomy (Section 5) discussed the task accomplished by the analyzed implementations. We found eleven main types of assignments, which differed significantly from one another. Implementations stretched from tasks of 3D model reconstruction and floor plan segmentation through indoor navigation and path planning to change detection and many others. Our subjective perception of the ongoing trends in the research was validated in the bibliometric analysis performed. Its results (Section 6) confirmed all the initial assumptions of a visibly growing trend in the number of publications that (1) discuss the topic of room segmentation and classification, (2) share similar basic processing, and (3) are strongly related to each other. The categorized knowledge also allows the reader to observe frequently discovered challenges, which seem to indicate the most common difficulties that researchers should be aware of and try to overcome (Section 7). In summary, this research indicates that all environments can be digitalized and somehow facilitate the automation of manufacturing and controlling processes. The range of available data types and their possible applications is so vast that practically every enterprise is able to find a solution suitable for meeting its needs and capabilities. We have researched, described, and summarized recent solutions that could help them gather these data, understand them, and use them to better manage their resources. We did this with a particular focus on one specific resource management aspect--facilities' digitalization and their space organization. We wanted to know what type of input data can be processed, how, and for what reason. After reviewing such a broad collection of papers we can conclude that the diversity in the input data structures and the processing methodologies should be considered not as a problem but as an opportunity. We can choose the data representation that is best for our use case. We can adjust it to the hardware limitations,business applications, or even our own convenience. Researchers should not restrain data reprojection, as it may significantly simplify the solution. The limitations of this work are, at the same time, the indicators of potential future research. Because of a very wide range of covered applications and described solutions, their presentation neither goes into algorithmic details nor discusses implementation nuances. The expected continuation of this review would be to analyze its results once again and answer additional research questions, focusing on the exact methodologies presented in the found solutions. Instead of answering 'what' and 'why', it would also be of high value to elaborate on 'how' things could be achieved.