# Semantic Information for Robot Navigation: A Survey
## 2 Acquisition of Semantic Information in Robot Navigation
### Fully Automated Information Acquisition
#### 2.2.1 Understanding the Environment
[29] included the detection of legs proposed by [PERSON] et al. [46]. The diagram of the system is represented in Figure 2. The approach gets data from the room noise using microphones connected to an Arduino and the movement of people in the environment through the detection of legs. By combining data on a Support Vector Machine (SVM), the system is able to differentiate different types of rooms. A recent work exploits other kind of environmental data, combining information coming from an artificial nose with visual information [47]. By doing so, the system is able to detect objects that emit certain kinds of gas. Authors argue that this system could be useful in certain indoor environments where information associated with these kinds of objects may help exploiting new semantic relationships. **Labelling**  
In the last decades, a number of researchers have proposed methods to label rooms or areas where the robot can move. Labelling consists of naming such room or region to uniquely identify it with respect to other regions, and also to attach it to a category that provides certain properties that can be taken into account in the navigation process. The information provided by each category depends on the level of abstraction allowed by the detection and classification process and the ontology used. This means that the perception capabilities of the robot are directly related to the level of abstraction provided by the labelling process. [PERSON] and [PERSON] faced the problem of semantic navigation by breaking it down into discrete tasks [48]. This work addressed the objectives of place recognition and region categorization. The navigator uses machine learning techniques to manage dynamic changes in the explored environment and this work assumes that the robot should be able to classify and label all places. The semantic navigator also uses information related to the category of the place. The categories that can be handled depends on the complexity of the system. The categories can be therefore simplified to _room_, _hall_, _door_ or can model more complex information, leading to more detailed representations such as _kitchen_, _living room_, _bedroom_ for defining rooms. In any case, the cognitive navigator relates categories to regions of the environment. Literature on methods of labelling places for robot navigation is wide. [PERSON] et al., discussed the differences between labelling in indoor places and outdoor ones since in the latter usually there is no clear separation between different places [49]. This work uses RGB-D information for semantic mapping, including a labelling layer that relies on a Random Forest classifier. [PERSON] et al., presented an automatic robotic system that generates semantic maps in retail environments using point clouds  
Figure 2: Office 3D semantic map renderings [29]. to recognize and label objects [50]. From the 3D information, the system uses SIFT features to perform classification. In labelling, there is a trend that intend to identify regions of interest in the environment, such as floor, walls or doors [51]. But, although this endows the system with some knowledge regarding navigation, it does not categorize the place. This task was addressed in [PERSON] et al. [52] where the system differentiates between halls, offices, reading rooms and doors. Besides, the authors weighted the advantages and disadvantages of using different sensors for the semantic labelling of places. Another work used Scale-invariant feature transform (SIFT) [53] features to characterize different areas [54]. This information was combined with a probabilistic description logic. [PERSON] et al., presented a preliminary proposal for a toposemantic navigation model based on visual information for indoor environments [55]. This system relies on region detection at the lowest level and use this information to grow on abstraction, adding information from objects detected in the scene.