# Semantic Information for Robot Navigation: A Survey
## 6 Conclusions  
This contribution reviews the state of the art in mobile robot navigation paying special attention to the role of semantic information. From this idea, a high-level navigation category arises, that is, semantic navigation that can be defined as the ability of a robot to plan the path to its destination taking advantage of the high-level information provided by the elements of the environment. Exploiting this information in the real world allows more optimal navigation. To achieve high-level navigation, some previous steps must be considered. First, information acquisition allows identifying the main components in the environment following two procedures. This process can be easy by interacting with humans, who will provide the information pieces required or, conversely, the robot can autonomously detect people, objects and other elements in the environment. This ability to acquire information is an open problem that has been addressed in the contributions mentioned in the present work. In the case of autonomous detection, the elements found in the environment will be limited by the sensors the robot includes. There is a difference, therefore, between acquiring information directly from the environment and acquiring knowledge from interaction with humans since this last option allows more abstract relationships. The first case deals with the idea of recognizing objects, shapes, corners, or any item suitable for obtaining information from the environment. In this case, multiple techniques for object detection, segmentation and frame analysis have been included. In the second case, it deals with the idea of the system being able to learn new concepts, such as relationships. The most widespread technique among the authors facing this problem is the robot's dialogue with a human user. Through this dialogue or interaction with the user, the robot learns new concepts. Interaction with humans is, therefore, a useful ability from two points of view. On the one hand, through the interaction the system recognizes the objective of the navigation. On the other hand, using interaction, the system can learn some concepts. From the works presented in this paper, it can be stated that interaction by voice and dialogue predominates. However, some authors choose a display or keyboard interface. In this paper, different types of maps have also been reviewed. These maps can be used in semantic navigation. Two types of maps can be distinguished according to what different authors have been discussing, that is, semantic and cognitive maps. All authors agree that the way of mapping the environment define the navigation to be carried out. As more general conclusions, it is observed that the bio-inspired models are important. An attractive feature for semantic navigation is that it works with abstractions similar to those performed by humans to plan their path and classify their environment according to their usefulness. The ability to reason is one of the main capabilities of a high-level navigator. The more concepts it can manage, the more knowledge it can extract. This allows that even when there is very little information, non-specific information or very abstract information, the robot reaches its destination. The works that have included concepts of high-level of abstraction have used ontologies that allow them to manage conceptual hierarchies. In this case, some reasoning system is added. These systems extract information and make inferences about the defined ontologies. The systems reviewed include a series of reasoning techniques, such as behaviour trees, finite state machines, reasoners based on descriptive logics and reasoners that access relational databases. These reasoners are identified as flexible and powerful, while those that access relational databases are faster and can work with a larger volume of concepts. Regarding the components that a semantic navigation approach has to implement, different authors identified some common grounds.