# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 7 Challenges  
The extensiveness of the conducted survey allowed us to detect some common problems and challenges that the researchers had to overcome in their studies. They differ from one analyzed solution to another but, at the same time, are a good indicator of what frequent difficulties may be encountered during the research, independently from the low-level accomplished task. We specify a set of five such challenges and describe them in this section. The most important one (in our opinion) seems to be aligned with the topic of this publication. It is the diversity of input data and their representation. During the research, we have pointed out many main types of data but it is still not enough. The real challenge here can be easily described with the use of building floor plans. They are not unified. We can find multiple different styles of plans. They can be monochromatic or colorful, simplified for easier understanding or filled with technical details for constructors, empty or filled with furniture pictograms--even the same building can have many different floor plan representations. The same can be said about other data types, e.g., spatial models being mesh models or point clouds. The second and third challenges are related and should be discussed together. The first problem is the amount of data that needs to be collected, stored, and processed. Nowadays, researchers have to expect millions of samples and terabytes of measurements. They need to have a plan regarding how to receive it fluently, save it, and prepare it for later use. The second topic is how to overcome these issues while working with very limited hardware resources. It works fine when more computing power and better equipment come together with the increasing volume of data. An example here can be the process of professional 3D modeling in digital twin reconstruction. Although a scan of a building delivers massive amounts of data and requires powerful computers, they are available and used to work with all the acquired measurements. But this is not always the case. On the one hand, the growing interest in smart solutions results in a rapidly increasing number of sensors used and providing data, but on the other hand such sensors are not powerful in the aspect of processing capabilities. The idea of edge computing seems to be an urgent topic for extensive research, as the  
Figure 17: Diagram of link-references between articles of the extended bucket of papers processing 2D Images. In decreasing order, from top to bottom, papers with the most links: ([1,86,87,94,112]). data volume increases rapidly and the sensors are forced to be small, cheap, and with long battery life, restricting their computational power. The fourth discovered challenge is dedicated to human participation in data gathering. As mentioned in the introduction to this research, the best scenario is achieved when the proposed solution requires little to no user involvement. As we noticed in the found articles, not every solution followed this assumption. In multiple cases, the data collection process required a researcher to spend many hours walking with a scanner or taking photos of the environment. As the operator's time is of increasing value for the whole process, newly created solutions will have to face the requirement for fully autonomous data acquisition and processing. This could be achieved with robots, drones, or smart sensors. The last discussed visible challenge is information loss. It may seem obvious, as the semantic meaning reconstruction is the main topic of this research, but it needs to be stated clearly--in some representations, specific data are irretrievably lost. An example here is a floor plan with no information on floor height. We could find multiple different ways to recreate information, but none of them can be accepted as an indisputable and always correct way of information retrieval. While preparing an implementation, researchers should be aware of the limitations present in the selected data type.