# Semantic Information for Robot Navigation: A Survey
## 1 Introduction  
In recent years, there is a growing interest in adding high-level information to several robotics applications to achieve more capable robots, even able to react to unforeseen events. Following this trend, the mobile robotics field is starting to include semantic information to navigation tasks, leading to a new concept: semantic navigation. This type of navigation brings closer the human way of understanding the environment with how the robots understand it, providing the meanings to represent the explored environment in a human-friendly way [1]. People identify the place where they are both spatially and conceptually. If a person moving through an indoor environment is asked about the trajectory of their displacement, they will not answer in terms of nodes or coordinates. Instead, people tend to provide concepts such as \"I was in the living room and I went to the kitchen to drink a glass of water\". In this regard, there are efforts towards including semantic concepts of the environment in robot navigation. This can be achieved by implementing cognitive maps, an approach able to encode information about the relationships between concepts in the environment. Concepts are high-level (abstract) entities that group objects(e.g., chair, table, bed) and places (e.g., bedroom, office, kitchen), utilities (e.g., used for sitting) and the relationships between them. Objects and places detected in the environment are translated into physical entities. These entities can also be related to concepts, such as the utility of an entity. This kind of representation allows robots to build maps that can be understood by humans, thus reducing the gap between geometric interpretation and high-level concepts. Semantic maps provide a representation of the environment considering elements with high-level of abstraction [2]. These possess different meanings for humans including the relationships with spatial elements used in low-level navigation systems. Semantic navigation requires connecting the high-level attributes with the geometric information of the low-level metric map. This high-level information can be extracted from data coming from a range of sensors, allowing the identification of places or objects. Adding semantic meaning to the elements in the scene and their relations also facilitates human-robot interaction (HRI) since the robot will be able to understand high-level orders associated with human concepts. For instance, [PERSON] and [PERSON] applied these concepts to understanding natural language interactions when a person requests a robot to find a new object and the robot must look for that object in the environment [3]. Therefore, adding semantic knowledge in mobile robots navigation tasks poses an important advance with respect to traditional navigation techniques that generally use metric [4; 5; 6], topological [7; 8; 9], or hybrid maps (a combination of the previous two ones) [10; 11]. When adding this high-level information to navigation systems, the first issue to address is how to represent knowledge. In this regard, Ontologies are one of the best ways of obtaining high-level representations of knowledge through hierarchies of concepts [12]. Since commonly concepts of entities, such as objects, tend to be separated from physical and real objects in the environment, spatial and conceptual hierarchies arise. [PERSON] et al., propose to make two categories of hierarchies [13]. The spatial hierarchy contains metric information of the environment, and its nodes represent open areas such as rooms or corridors. The arcs represent the possibility of navigating from one node to another. The conceptual hierarchy models semantic knowledge from environment information. In this approach, all concepts derive from the _Thing_ entity. This is the highest-level concept from which the rest of concepts in this architecture derives. The hierarchy has three levels, with _Thing_ in the first one.