# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.7 Room Classification
In their scenario, a room type was recognized from a limited number of inputs--a view recorded only by a depth-aware camera situated at the door frame while entering the room. Real-time scanning was the topic analyzed by [PERSON] et al. [73]. They focused on a moving robot equipped with adjustable cameras. The main objective was to optimize the sensor line of sight and maximize the amount of collected data. They combined the processing of gathered knowledge, like robot paths, detected objects, and taken pictures, for quick and accurate place categorization. Sound echoes, chirping, and radio frequencies were the input data types for which we found four articles. The oldest one, by [PERSON] et al. [201] from 2012, presented a system that identified the room type in an audio sample or video recording. Each sample included acoustic features and came from a musical signal or a speech recording. The proposed solution was based on a Gaussian Mixture Model (GMM). In 2018, [PERSON] et al. [202] published an article exploring the usage of a smartphone's loudspeaker in indoor localization tasks. The presented solution classified the room by analyzing the echoes recorded in response to a generated inaudible chirp. The best accuracy was reported while using a two-layer CNN processing spectrogram of recorded echoes. A similar solution was  
Figure 11: Visualization of the Room Classification task. A sample input floor plan (**left**) is analyzed and searched for room areas. Each area is classified and marked with a room-type label (**right**). presented by [PERSON] et al. (2020) in 2020. They proposed using a smartphone with a dedicated application to capture room acoustic profiles. The app generated and transmitted acoustic signals that reflected from objects in a room, creating a unique profile of a room. Learned features were later used by a classifier for room recognition. The newest work by [PERSON] et al. (2020) was focused on Augmented Reality (AR) applications. In their research, the authors analyzed the task of virtual sound generation. They proposed combining a machine-learning system based on a CNN with Binaural Room Impulse Responses (BRIRs) to provide possibly realistic room reverberations in an augmented environment. Laser range readings represented a specific type of feature set. Known for environment picture processing, the hierarchical approach was presented in the research of [PERSON] et al. (1975, 1988). In their first publication, an algorithm called Spatial Hierarchy of Parts (sHoP) was used to construct a representation of the environment from laser scans. Initial measurements were transformed into images, which were hard to understand, even for a human. For room classification, the authors used a Histogram of Compositions (HoC), and the whole solution consisted of three processing layers. In the later article, the idea was further developed, and on top of the hierarchical processing was the Multi-Category-Affinity-Based-Exemplars algorithm added as a fourth layer, reporting better performance. In 2018, [PERSON] et al. (1979) presented a machine-learning approach. In their work, a laser range scan of a mobile robot was formatted into polar images, which were further processed by a technique named Local Receptive Field-Based Extreme Learning Machine (ELM-LRF). It effectively merged the ideas of splitting image processing into its sub-parts (receptive fields) and using an extreme learning machine (a neural-network machine with just one hidden layer). The Mixed input was represented by two articles. In 2018, [PERSON] et al. (2018) proposed using sparse observations of the environment and openly accessible data in combination with a Bayesian Classifier. They could infer floor room shapes and their functions through the room area and its direction (orientation). [PERSON] et al. (2018) used a similar yet extended information set. They used training data consisting of area, length, width, and room type with a created set of grammar rules and a geometric map of the floor to estimate the semantic meaning of the rooms.