# Semantic Information for Robot Navigation: A Survey
## 5 Open Issues and Questions  
An extensive review of literature in semantic information for robot navigation raises some interesting questions that practitioners should take into consideration.  
Is a knowledge representation model better than another?When comparing between knowledge representation models it is important to establish comparison criteria. This is still an open problem since abstract representations of information are not easy to evaluate. Experts in psychology have not reached a consensus about the knowledge representation that our brain uses to process information. In fact, multiple analogies have been used to try to understand our internal representation.  
How essential is an object recognition mechanism in navigation?In general, proposals for semantic navigation include a object detection mechanisms. Most of the semantic navigators need information about the objects in the scene to provide high-level classification of places. Therefore, an object recognition mechanism is a key feature to integrate into such systems and it is desirable having a system as generic as possible, that is, without the need to be trained to detect all specific objects in the environment. For instance, a system should be able to recognize a new instance of an object (e.g., a chair) in any environment without specific training for that chair. New machine learning techniques are leading the way to achieve a high level of generalization, but at the cost of great amounts of data for training [121; 122].  
Are there environments more suitable for semantic navigation than others?This question has been discussed by many researchers. Despite its advantages, it is not clear yet how robots that use current semantic-based techniques for navigation will coexist with humans. One of the concerns regards the number of people in the environment as robots using high-level semantic navigation rely on object detection. A crowded environment is more prone to multiple occlusions, thus reducing the chances for information acquisition. Some authors propose solutions to this problem, but still extensive experiments are necessary to define the kind of environments more suitable for this type of navigation techniques.  
Indoor semantic navigation methods are reusable in outdoor environments?This issue is usually arises when a new semantic navigation system emerges. In the state of the art, there are semantic navigators for indoor and also for outdoor environments, but are they so different? It is reasonable to consider whether the progress in either application is usable on the another. The question that remains open is whether a navigator will emerge that serves both for indoors and outdoors, having defined ontologies wide enough to consider the two levels of relationships and the functional sensory ability for both situations.  
_What is still lacking to see robots in everyday environments such as homes, hospitals, etc.?_ Semantic navigation involves several disciplines of robotics. For this reason, aspects such as object recognition and human-robot interaction must be integrated with a robust low-level navigation system.  
_What applications, other than navigation, can benefit from the same semantic map scheme?_ The management of semantic knowledge and the management of ontologies can be applied to other tasks apart from navigation. The RoboEarth project includes a knowledge system implemented with Prolog that allows a robot to accomplish tasks like cooking a pizza. Therefore, it is reasonable to think that the inclusion of semantic maps can be useful to develop other tasks. It can also be applied to user interaction to obtain information of any kind, not only relative to the navigation. Besides, object detection can be improved with the inclusion of the semantic map. This means that many applications can be improved by integrating techniques described in this work.