# Semantic Information for Robot Navigation: A Survey
## 3 Representation of Semantic Knowledge
### Semantic Maps  
Semantic mapping is the map that takes into account complex semantic concepts of the environment, such as objects and their usefulness, different types of rooms and their uses, subjective sensations transmitted by a place in a user, etc. These concepts are related to each other offering valuable information about the environment that is reflected in the semantic map. Being aware of the implications of the choice of technology and the level of abstraction to be used in the mapping, [PERSON] et al., considered of great importance the application that has the mapping in service robots and present a hybrid model of the semantic map [98]. Classifying the navigation maps into two categories: the metric maps and the topological maps. To reach a map with the advantages of both categories and reducing the limitations, the hybrid maps [99] outstand. All belong to traditional maps and focus on representing the geometric structure of space, describing the quantitative coordinates and connectivity between locations. However, the functionality of the locations and the complexity of the partial space are not considered. Nor are high-level semantic concepts used to interact with people. This situation where the robot planner, location and navigation based on these types of maps do not meet the needs of the robot's service tasks, motivated the definition of semantic maps [98], as a more suitable option than cognitive maps. The paradigm of the development of semantic maps used by [PERSON] and [PERSON] is based on space properties [17]. These properties can be described as attributes that characterize discrete space entities identified by the robot, such as places, rooms, or locations. Additionally, the properties make them correspond with human concepts and provide another layer of spatial semantics shared between the user and the robot. Each property is connected to a sensory information model. High-level concepts, such as room types, are defined by properties. Figure 6: Cognitive map modules based on visual memory [94]. * Objects. Each object class corresponds to a property associated with the place. A particular location is expected to display a certain number of a particular type of object, and a certain amount of them is observed. * Door. Determines whether a location is determined by a door. * Shape. The geometric form of the room extracted from the information of laser sensors. * Size. The relative size of the room extracted from the sensory information of the laser. * Appearance. The visual appearance of a place. * Associated space. The amount of free space visible around a placeholder not assigned to any place. Conceptual map is shown in Figure 7. Every specific instance of the room is represented by a set of random variables, one for each property associated with that place. Entering into concepts of creation of semantic maps (without being the same as a cognitive map), some works explore this concept and its application in mobile robotics. [PERSON] and [PERSON] proved that semantic knowledge can help a robot in its task of reaching a destination [83]. And part of this knowledge has to be due to objects, utilities, events or relationships in the robot environment. The data structure that supports space-related information about this environment is the map. A semantic map increases the typical geometric and/or topological maps with information about entities (objects, functionalities, etc.) that are located in the space. This implies the need to add some mechanism of reasoning with some previous knowledge. In this way, a semantic map definition is reached:  
A semantic map in a mobile robot is a map that contains, in addition to spatial information about the environment, assignments of mapped features of known class entities. In addition to the knowledge of these entities, regardless of the content of the map, some kind of knowledge base must be available with an associated reasoning engine for inferences [83].